FINAL REPORT
Development of mobile device-based surrogate systems for connected and autonomous vehicle technologies
NSERC Engage (EGP 477034-14) OCE VIP (No. 22905) FINAL REPORT
Prepared By: Xintao Liu, Postdoctoral Fellow,
[email protected] Shadi Djavadian, PhD student,
[email protected] Wei Huang, PhD student,
[email protected] Joseph Y.J. Chow, Principal Investigator,
[email protected] Simon Foo, CEO of industry partner,
[email protected]
Department of Civil Engineering, Ryerson University 350 Victoria Street, Toronto, Canada, ON M5B 2K3 February, 2016
FINAL REPORT Project Summary Connected and autonomous vehicle technologies have great potential, but the cost of setting up field tests with original equipment can be high. We develop a suite of Android-based mobile apps and server-side programs on the Web to work together to mimic the computing and communications technologies behind some features of connected and autonomous vehicles. We deploy server side programs on the web server of a transportation analytics company in Toronto, ON, and develop Android-based mobile apps on Google Nexus 7 tablets, which serve as “surrogate systems” to enable users to inexpensively and safely collect travel data for performance measurement. As part of the project we designed and developed a travel prediction model and designed the framework for a cooperative route guidance system. The “surrogate systems” provides industry partner Transnomis with tools to expand their market offerings by allowing them to conduct “in situ” tests on the field in an inexpensive and safe manner for the public, and thus meet the challenge of emerging connected and autonomous vehicle technologies.
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FINAL REPORT Table of Contents Project Summary.............................................................................................................................. i 1.
2.
3.
Introduction ............................................................................................................................. 1 1.1.
Background ...................................................................................................................... 1
1.2.
Objectives ......................................................................................................................... 3
Designing mobile apps for CV/AV......................................................................................... 5 2.1.
Existing system of Transnomis ........................................................................................ 5
2.2.
Designed CV/AV architecture ......................................................................................... 6
Travel time prediction models ................................................................................................ 8 3.1.
Short-term traffic forecasting overview ........................................................................... 8
3.2.
Floating car data ............................................................................................................. 11
3.3.
Autoregressive integrated moving average (ARIMA) ................................................... 12
3.4.
Online support vector regression (OL-SVR) ................................................................. 16
3.5.
Ensemble method for switching prediction models ....................................................... 18
3.6.
Traffic prediction apps ................................................................................................... 19
4. Distributed Route Guidance ...................................................................................................... 21 4.1. State-of-the-Art .................................................................................................................. 21 4.2.
Distributed Anticipatory Route Guidance Framework ............................................... 25
4.2.1. Ad hoc vehicle group formation ................................................................................. 25 4.2.2. Information sharing ..................................................................................................... 27 4.2.3. Travel Time Prediction from Distributed Information ............................................... 28
5.
4.3.
Non-Technical Issues & Acceptance .......................................................................... 29
4.4.
Route Guidance app.................................................................................................... 30
Conclusion ............................................................................................................................ 33 5.1.
Deliverables ................................................................................................................ 33
5.2.
Future directions ......................................................................................................... 34
6. References ................................................................................................................................. 36
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1. Introduction 1.1.
Background
In recent years, technologies have enhanced the ability of vehicles to “sense” information, to “think” and process that information, to “act” upon that processed information (e.g. adaptive cruise control, collision warning, route guidance), and to “communicate” that information to other travelers, vehicles, or infrastructure. In particular, communications technologies (connected vehicles, CV) or artificial intelligence technologies (autonomous vehicles, AV) have made significant strides. For example, the U.S. federal government is adopting connected vehicle technologies for safety benefits (NHTSA, 2014). MTO is currently conducting a pilot project to safely test autonomous vehicles (MTO, 2013). The Ontario Research Fund (ORF) has supported Dr. Leon-Garcia in research on “connected vehicles and smart transportation”. The private sector is developing fully autonomous vehicles (CBC, 2014) while pilot research projects are being conducted on connected and autonomous vehicle systems (Toulminet et al., 2008; Bertolazzi et al., 2010; Chan et al., 2012; Virginia Tech, 2014). Despite all the development in the technologies themselves, methods to evaluate their performance and effects on social welfare are more limited. Interest is clearly there (e.g. MTO, 2013), but most methods rely on computer simulation models (e.g. Fernandes and Nunes, 2010; Dion et al., 2011; Arnaout and Arnaout, 2014; Olia et al., 2014). The problem with simulations is that they have strict assumptions about traveler behaviour, information available to travelers, and environmental effects like weather or road conditions. On the other hand, field experiments like those conducted by Hirai et al. (2005) or the earlier automated highway research conducted by the California PATH program in the 1990s (Shladover, 2006) require much larger investments in the technology. An alternative solution is to use surrogate systems based on mobile devices that mimic some of the technologies so that different designs can be cheaply evaluated without resorting to full investments (Harvey et al., 2016). Mobile devices such as the Google Nexus tablet series can handle communications between each other as well as with a centralized facility/infrastructure, data processing, sensing, and even actions like fare payments through local communications protocols like Bluetooth or Near Field Communications. Mobile phones have been successfully
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FINAL REPORT deployed as a traveler trajectory sensor in the private sector (Google, Qualcomm, among others) as well as in academic research (Herrera et al., 2010). Obviously, some technological functions (for example, automated collision avoidance) cannot be replicated on the field without the actual technology, but what can and cannot be done is still not well understood at this time. While mobile devices have been used as sensors, as data processors, or as communication devices, development has not yet been made for a well-defined suite of tools that mimic—as surrogates of—actual CV/AV technologies and collect the data to measure their performances as well. From the perspective of Transnomis, this gap in research poses as a significant market opportunity with which they do not currently have the resources or expertise to enter. Transnomis develops and markets commercial off-the-shelf central system software for Advanced Traffic Management Systems known as Mirasan, shown in Figure 1, as well as perform custom development and systems integration work for its customers. Mirasan provides a unified interface for Traffic Management Centers (TMC) and emergency responders to manage roadway issues (e.g., accidents, constructions, and special events) and roadway equipment (traffic cameras, vehicle detectors, electronic message signs and weather stations).
Figure 1. Mirasan system at https://www.mirasan.ca
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FINAL REPORT With the push towards connected vehicles and potentially distributed information, Transnomis currently does not possess any products that cater to those uses. In order for them to enter that market, they need to develop IPs related to connected vehicle technologies, as well as related to mobile device interfaces. This research project serves both purposes by developing mobile device-based software for evaluating technologies used by connected vehicles and distributed information schemes. These technologies help Transnomis reach out to the end user as a new market. Furthermore, the technologies give Transnomis an opportunity to license the software to other traffic information providers, to vehicle manufacturers interested in using the surrogate systems to evaluate their technologies, and to public agencies interested in customizing the surrogate systems to cheaply evaluate new technologies on the horizon.
1.2.
Objectives
The aims and objectives of the project are briefly summarized as follows. a) Establish tablet-based architecture to evaluate the connected and autonomous vehicle technologies b) Short-term Traffic Forecasting
Data acquisition from Mirasan
Selection of prediction algorithm
Training and testing the selected prediction algorithm
Integration of algorithm and mobile device interface with Mirasan
c) Applications of vehicle to vehicle communication and route guidance with distributed information
Literature review
Designing a distributed route information system framework for Transnomis
This project consists of three primary contributions conducted with Transnomis. First, we define a consistent architecture for “surrogate connected and autonomous technologies”, derived from the architectural modifications proposed in Harvey et al. (2016) for generic cyber-physical transportation systems. The architecture is based on defining surrogate functions of the Canadian ITS architecture (ITS Canada, 2012) for key autonomous and connected vehicle technologies revolving around the four key roles: sensing, processing, communicating, and acting. For each role, the feasibility of surrogate systems for different user services is evaluated. In our first step, 3
FINAL REPORT we customize that functional architecture to leverage the data that Transnomis collects for the two software technologies that we developed for them. The second and third contributions focus on design of two features for Transnomis to market to their customers using the architecture. The first is a surrogate system to test decentralized traffic prediction algorithms necessary for connected vehicles to operate during times when failures in the transportation network may prevent effective centralized control. The system allows Transnomis to help their clients decide which designs are best to operate; for example, multichip protocols for relaying information, data structures to combine multiple sources of data with partial centralized real time information (such as that which Transnomis collects). The other surrogate system involves distributed route guidance, and in particular, evaluating operational designs related to cooperative autonomous vehicle fleets. For example, design issues include incentive design for vehicles to cooperate with other vehicles for information sharing. The data for real time travel times of different routes (both for passenger vehicles and for busses) in the network are obtained from Transnomis. The research allows Transnomis to help their customers determine which technological designs are more conducive to effective traffic operations using connected vehicle technologies during that time.
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2. Designing mobile apps for CV/AV 2.1.
Existing system of Transnomis
Transnomis Solutions’ product Mirasan collects data from various open and private sources and provides a unified interface to visualize and analyze transit system, which focuses on visualization along with a series of analytical functions such as travel time and a dashboard. In Figure 2, we list the data sources of this centralized web mapping and analytic system, summarize its main functions, describe potential users and build up the relationships among them. This figure aims to better understand the framework and components in the Mirasan system, based on which we would be able to add more functions and improve its performance.
Figure 2. Data sources (left), main functions (middle) and potential users (right) in Mirasan system.
Mirasan provides a platform towards relief of traffic congestion, improving road safety, etc., which will be beneficial for users from different fields (Figure 2). Based on this platform, we 5
FINAL REPORT use its data sources and existing functions as well as other available external data sources to add a short traffic prediction function to the Mirasan system.
2.2.
Designed CV/AV architecture
The surrogate system can mimic logical architecture processes to reduce costs of ITS deployment, as shown in Figure 3.
Figure 3. Comparison of (left) current ITS development with (right) proposed surrogate system-based ITS development. (Source: Harvey et al., 2016)
We identify the subset of ITS logical architectural processes pertaining to CV/AV technologies that can be mimicked using tablet devices in Table 1. Table 1. Capability of surrogate systems to mimic CV/AV technologies (adapted from Harvey et al., 2016) Logical Architecture Processes
Capability of Proposed System to Mimic*
Provide
CF-centralized monitoring, local monitoring and sensing
Vehicle
Monitoring
and Control
V-route guidance algorithm
Manage Transit
CF-centralized fleet management V-communications to/from CF, location, real time operations, fare transactions, passenger count/load, vehicle driving behavior
*
CF – central facility, V – vehicle
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FINAL REPORT By integrating the surrogate systems with Mirasan, the following functions are added to their system shown in Figure 4. The centralized application enables traffic forecasting and navigation service via route communication with the centralized server in real time; the distributed application enables users to sharing information directly.
Figure 4. New functions (in red circles) in Mirasan system
Sections 3 and 4 describe the traffic prediction component in a decentralized environment, and the framework for the cooperative route guidance, respectively.
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3. Travel time prediction models 3.1.
Short-term traffic forecasting overview
In the past 35 years, a large number of studies and a voluminous literature have been devoted to short term traffic forecasting, with Ahmed and Cook (1977) being among the first researchers in this area. The majority of these studies have focused on developing methodologies that can be used to model such traffic characteristics as density, speed, volume, and travel time in order to predict anticipated traffic condition for the next few seconds to a few hours based on historical and current conditions. The existing literature is comprehensively reviewed in four papers; Vlahogianni et al. (2004) provided a critical review of the entire spectrum of short term traffic forecasting up to 2003, where they underlined complexities of several conceptual, design, and methodological issues. Adeli (2001) and Van Lint & Van Hisbergen (2012) reviewed neural network and artificial intelligence applications to short term traffic forecasting, collecting and analyzing the literature using such approaches. Recently, Vlahogianni et al. (2014) summarized and categorized the existing literature, highlighting challenges and future research directions in short-term traffic forecasting. Past studies mostly focused on traffic flow prediction as opposed to travel time prediction. The ones who focused on travel time prediction developed models for freeways as opposed to the whole network including arterials. Previous travel time models focused mostly on freeways because it was easier to collect travel time data for freeways by using loop detectors, which is not an efficient and feasible way of collecting travel time for arterials. With advances in communications and GIS technologies in the recent years, researchers started focusing on travel time prediction using floating car data. The advent of connected vehicles will help speed up the research in network wide distributed travel time prediction. In summary, classical methods of short term traffic forecasting used statistical approaches to predict traffic at single points. These methods however are shown to be weak and inadequate under unstable traffic conditions and complex road designs. In recent years, computational intelligence-based approaches have been gaining more interest among researchers, including neural networks, Bayesian networks, fuzzy and evolutionary techniques. 8
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There are numerous machine learning algorithms for classification, regression and pattern recognition. Support vector machine (SVR) is one of the most used algorithms. The main idea of SVM is: (1) to represent samples geometrically (by “vectors” as shown in stars and red points in Figure 5) and (2) to find a linear decision surface (“hyperplane”, red line in Figure 5) that can separate sample classes and has the largest distance (i.e., largest “gap” or “margin”) between border-line samples (i.e., “support vectors”).
Figure 5. Main ideas of Support Vector Machine (SVM) (Source: Simon and Melton, 2014).
Jones et al. (2013) used SVR for short term travel time prediction using probe vehicle data and showed that it outperforms traditional travel time prediction models. An alternative method is the Online Support Vector Regression (OL-SVR), which is designed for online training to be more responsive to real time data. Jeong et al. (2013) used OL-SVR for traffic flow prediction and compared it to other methods (e.g., regression methods, artificial neural network and time series analysis methods) for traffic flow prediction. They concluded that OL-SVR outperforms other methods due to the following reasons:
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Based on structural risk minimization – i.e. the model selection process includes a balanced consideration of overfitting and accuracy
Independent of dimensionality of input space
Best for atypical conditions
Adaptive to complex systems and robust in dealing with corrupted data.
There are no studies that have used OL-SVR for short term travel time prediction using probe vehicle data. For ITS where new data is obtained every few second, real time training such as OL-SVR is needed. Therefore, we evaluated OL-SVR in the course of this project as a short term traffic prediction model. Different options for travel time prediction architectures using floating car data are presented in Figure 6. Specifically, two scenarios, recurrent and noncurrent, can be taken into account for travel time prediction. Travel time prediction can be link-based or path-based and each have their own advantage and disadvantage over one and another. Link based travel time prediction is often used for en-route routing where enumerating the whole path is not necessary or is computationally extensive. Path-based travel times are often used by a centralized sever which has access to all the paths between origins and destinations in the network. Path travel times tend to be more accurate than link travel times because they take into account the delays at controlled and uncontrolled intersections and other delays on the link. Since Transnomis provided path travel times, those were used in the project. An autoregressive integrated moving average (ARIMA) model was selected to predict recurrent travel time as the classic approach pointed out in Vlahogianni et al. (2014). Near-real time training needs to be conducted for noncurrent scenarios in this project. OL-SVR was evaluated as the method for non-recurrent travel time prediction due to convenience of real time training. The OL-SVR model is designed to train from the most recent sample data to efficiently update the predictive model.
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Figure 6. Alternative Travel Time Prediction Architecture Options
3.2.
Floating car data The path travel time data is floating car data obtained from Mirasan.ca. The floating car data
is collected from Toronto Transit Commission buses, which automatically record travel information such as location, speed, travel time, etc. to server machine in a regular time interval, e.g., one minute, and provided to the public. The data is collected and processed by Mirasan to reflect passenger car data. For more information, please refer to Mirasan.ca. Figure 7 illustrates the sample data, which include: Time, Travel Time, Number of Samples and Quality Metric. The travel time is in units of minutes.
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(b) (a) Figure 7. Path-based sample data from Mirasan.ca; (a) the path (blue line: Eglinton from Allen to Yonge) on the map, and (b) travel time in relational data base.
3.3.
Autoregressive integrated moving average (ARIMA)
For the ARIMA model, sample data from two weeks’ travel time used included 10,080 data samples, since two-minute time intervals were assumed. This data includes imputed data using interpolation for intervals that were missing. Figure 8(a) indicates the path of a selected sample data on the map. The value of max, min, mean, median, mean, and standard deviation of the data is reported as 37.50, 9.69, 14.84, 15.82 and 4.09, respectively. Figure 8(b) illustrates the distribution of travel time over time. Cross validation was used to design the ARIMA model structure. The order of the autoregressive model was set at 𝑝 = 5, the degree of differencing at 𝑑 = 1, and the order of the moving average model was set at 𝑞 = 5, i.e. ARIMA(5, 1, 5). Figure 9 shows the data fitting based on ARIMA (5, 1, 5), and Figure 10 provides several performance measures of the model. The plot of the standardized residuals indicates that there’s no trend in the residuals, no outliers, and in general, no changing variance across time. The plot of the ACF of residuals shows that there are no significant autocorrelations. The bottom plot illustrates p-values for the Ljung-BoxPierce statistics for each lag up to 10. The dashed blue line is at 0.05. All p-values are above it indicating non-significant values for this statistic when looking at residuals. All these evidences show that the result are good. Moreover, the model was validated using 10 observations, shown 12
FINAL REPORT in Figure 11. The Root Mean Squared Error (RMSE) of the 10 observations is 0.599, which is only 4.9% of the mean of the observations.
40 35 30 25 20 15 10 5 0
1 405 809 1213 1617 2021 2425 2829 3233 3637 4041 4445 4849 5253 5657 6061 6465 6869 7273 7677 8081 8485 8889 9293 9697
Travel time (minute)
(a)
Time series
(b) Figure 8. (a) The path of sample data (Eglinton from Bayview Ave to Allen Rd); and (b) travel time distribution over time.
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Figure 9. Data fitting
Figure 10. Model diagnostics
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Figure 11. The predictive results
The validated ARIMA model was developed in R, and then wrapped into a Java library. Since the Transnomis system was developed with C#, the ARIMA model was finally transformed into C#. Figure 12 illustrates the designed interface. First, training samples can be loaded into the model by clicking the “Load Data” button, and the “Training” button starts training the loaded samples. After training, future travel time can be predicted based on inputted current time and a future number of time steps ahead of the current time. For example, in Figure 12(b), proximately 20.15 minutes of travel time is needed from 14:20 after 10 minutes (if each step is assigned to 2 minutes) on a specific path. No reliability measures are outputted, although the model is able to output such measures.
(b) (a) Figure 12. ARIMA model interfaces in two steps: (a) loading data, and (b) predicted results.
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3.4.
Online support vector regression (OL-SVR)
OL-SVR is used for travel time prediction for non-recurrent events. A modified dataset based on the one used for the ARIMA model was used to develop and test the model. The modified dataset included 20 simulated continuous data samples representing travel time when a nonrecurrent event happened. In the end, the value of these 20 data samples gradually increased by 10 to 20 for the first 10 samples and decreased back to 10 afterward. According to the mechanism of OL-SVR model, the original dataset for the ARIMA was used to train OL-SVR model first. The modified dataset and the trained OL-SVR model were inputted to the OL-SVR model again to re-train the model. However, the re-trained model was not able to fit the modified data sample well because of the negative effects from the rest of the samples having big data size. Nonetheless, we constructed the OL-SVR model so that Transnomis can conduct further tests with it. For the model that we constructed, we designed a simplified test to verify that it worked as designed. The framework is illustrated in Figure 13. Real-time travel time data from other users nearby would be obtained and used to train the prediction model using OL-SVR. If it is not the first time training, the most recently trained model is input to OL-SVR as well (this can be modified by Transnomis later). The whole training process is supposed to be conducted on a mobile device (front end). After training, the trained model would be updated and saved as a specific file for the next training. Based on the trained model, the near future travel time would be predicted.
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Figure 13. The work flow of non-recurrent travel time prediction
For the test case, a series of travel times are simulated based on a normal distribution with a mean of 40 and a standard deviation of 10, and sorted to simulate an event occurrence as shown in Figure 14. A 10-minute time window was used to define training samples. A total of five training samples based on 2-minute intervals were used for each training set and 20 data samples during 40 minutes were randomly generated. The two parameters, C and Epsilon, were set to 1000 and 0.01, respectively, for the simulated dataset. A Radial Basis Function (RBF) kernel was used for the OL-SVR model.
Figure 14 Simulation of 20 continuous samples reflecting a non-recurrent event
The first five simulated samples were used to initialize the OL-SVR model. The trained model was used to predict the travel time of the next moment, and the following simulated sample was 17
FINAL REPORT used as the ground truth of the prediction. Subsequently, the next five simulated samples were treated as new coming data that was used to re-train the model for this test case. The updated model was used for prediction, and the following simulated sample was used to validate it. This process was repeated until completing all the sample training. Figure 15 indicates the distribution of observations and the predictions. The Root Mean Squared Error (RMSE) of the 4 observations and predictions is 3.158, which is only 8.1% of the mean of the observations. The OL-SVR model was developed from an open source java library (link available at http://onlinesvr.altervista.org/Download.html). It was wrapped into Java to run on mobile devices. The model is called when new data arrives. The time window is allowed to be changed to any size, and the parameters of the model can also be estimated by using cross-validation.
Figure 15. The distribution of observations and predictions
3.5.
Ensemble method for switching prediction models
With the two types of developed prediction models, one model may be more applicable than another. One way to alternate between the two models as an “ensemble model”. Such ensemble models have been estimated in various ways. A basic way is to train a selection model based on a fixed threshold. When the attributes exceed that fixed threshold trained from real time data, the ensemble model would use one prediction model over another. For example, Ihler et al. (2006) compared such a base threshold model against the use of a more advanced Markov Chain Monte Carlo simulation to automatically detect unusual events in an observation sequence, and found the MCMC model to be a much better fit. Moreira-Matias et al. (2013) also experimented with an ensemble methodology, where different models were assigned probabilities of selection in a 18
FINAL REPORT decision tree, and those probabilities were trained from the observed data such that the ensemble made the best prediction. A decision tree is expected to be implemented in the future for Transnomis to enable automatically switching between prediction models under different scenarios (recurrent and non-recurrent).
3.6.
Traffic prediction apps
We developed an Android-based mobile app to communicate with the server to retrieve predicted travel times. The app sends GPS data (location and speed, and current time) to the server in order for the server to collect real time traffic conditions and use that for travel time prediction. For dynamic training real time data from vehicles is needed as well. Figure 16 is the main interface of the mobile app. Users may need to know the travel time for the path that they are on, so they can know what will be the anticipated travel time on their path and whether they need to change their path or not. To do so, users can just press the menu “Travel time” and then choose the OK button to get the travel time. If the user choose Cancel button, the retrieving process will stop.
Figure 16. Snapshot of mobile app to retrieve predicted travel time from server
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FINAL REPORT Figure 17 shows how the mobile apps and the prediction model on the server work together. It is a typical three-tier structure. Between each tier, we use Web API to share information between the server and mobile apps. On the server the standalone program is the ARIMA model and the DLL is the OL-SVR model. On the Vehicle tier are the functionalities of the mobile apps. Once the prediction is done, the developed mobile apps automatically read the results and display them on mobile apps for users.
Figure 17. Designing prediction apps for Mirasan system.
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4. Distributed Route Guidance One of the primary advantages available in a CV/AV environment is the availability of distributed information for individual vehicles to make informed decisions. To that effect, we design a preliminary framework for such a system for Transnomis to consider adopting in the future. These systems would allow Transnomis to work with, for example, AV providers like EasyMile and BestMile to operate autonomous vehicles in a decentralized setting. In this study, the focus on route guidance is on the benefits of having coordinated information and communications for individual decision-making (i.e. ad hoc vehicle group formation), as opposed to the mechanism design to incentivize AVs to form joint decisionmaking coalitions (see Shapley and Shubik, 1971; Roth, 1982; Kelso and Crawford, 1982; Sotomayor, 1999; Echenique and Oviedo, 2006; and Sönmez and Ünver, 2011 for discussions of coalition formation and stable matching; see Matsubayashi et al., 2005; Anshelevich et al., 2008; Agarwal and Ergun, 2008; and Wang et al., 2015, for applications in transportation networks). Mechanism design for joint decision-making will be considered in future research, for example, identifying incentives to induce individual vehicles to consider choosing routes that benefit the coalition as a whole as described in van den Bosch et al. (2011) and Djavadian et al. (2014). Three objectives are presented in this section: a summary of current state-of-the-art in the area of cooperative route guidance is provide; a comprehensive framework is proposed for implementation; a route guidance app developed for this project is presented.
4.1. State-of-the-Art Current personal navigation devices such as TOMTOM and WAZE provide users with a route guidance based on minimizing individual travelers’ travel time without considering the impact on the travel time of others. Provided that the traffic is calm and there is no congestion, this selforganization can be efficient and can also result in a balance situation at the level of the network, in which the available space is used effectively (Hoogendoorn and Bertini, 2012). However, self -organization has its limits. When the traffic situation declines, the traffic system is unable to self-organize efficiently. In fact, the opposite occurs, as shown by phenomena such as the capacity drop, self-emergence of inefficient start-stop waves, and suboptimal routing. The causes for these are only partially known, as they appear to branch from the autonomous behavior of 21
FINAL REPORT drivers. In fact, in many cases, the harder drivers try to follow their personal objectives, the more system performance reduces (Helbing et al., 2000). One solution to drivers’ self-organizing problem is for drivers to share their route information with each other either directly (V2V) (Du et al., 2014), through a centralized route information server (V2C) (Yamashita et al., 2005), or via roadside infrastructure (V2I) (Claes et al., 2011). Current navigation devices allow each driver to choose his/her fastest route to a destination in isolation without any knowledge of other drivers’ decisions or states. It is possible that several drivers choose the same path leading to congestion. However under cooperative anticipatory route guidance, the app allows drivers (which may also be an AI) to share their route information with each other. In this project we designed a framework for cooperative anticipatory route guidance based on V2V and ad hoc vehicle group formation that can be integrated with Mirasan (Transnomis system) working as a centralized route information server. This study was built upon the works of Yamashita et al. (2005), Claes et al. (2011), Claes (2015), Du et al. (2014) and Biddlestone et al. (2011). There are in general two types of ad hoc vehicle network formation: tactical and safety ad hoc vehicle network. In distributed systems where there is no centralized control system and a fixed real-time system administration support, tactical ad hoc networks between vehicles are formed to physically communicate with each other. Tactical ad hoc networks are designed for organizational groups. In tactical ad hoc networks vehicle move together towards a common goal. On the other hand, in a safety ad hoc network, neighboring vehicles frequently change and do not have inherent relationships with each other. In this study the focus is on tactical ad hoc networks. Biddlestone et al. (2011) introduced a distributed framework for multi-vehicle ad-hoc group creation as shown in Figure 18. In A, there are two distinct groups with their own goals. In B, a group follows a mobile goal. In C, a group is seen moving toward a goal. Vehicles form a group around a common goal. For example, vehicles at a traffic light or bus stop may create a natural convoy to allow as many vehicles as possible to proceed through the intersection during a green light. Group goals can vary. For example, a goal can be fast group formation and coordination, exploring and mapping, if the network has changed task of this group is to go out and then explore and record changes. The formation of vehicle ad hoc group can lead to efficient information exchange via multi-broadcast, when one group breaks and another group is formed 22
FINAL REPORT to exchange information. For more information on vehicle ad hoc formation, readers are referred to Biddlestone et al. (2011).
Figure 18. Sample tactical group formations (Biddlestone et al, 2011).
Since 2003, many researchers have studied the potential for distributed traffic information systems (e.g. Minciardi and Gaetani, 2001; Ziliaskopoulos and Zhang, 2003; Yang and Recker, 2006; Krajzewicz et al, 2008; Lee and Park, 2008; Katan et al., 2012). This section provides an overview of current studies on cooperative route guidance, namely: Claes et al.’s (2011) work on distributed anticipatory route guidance, Du et al.’s (2014) work on a coordinated online invehicle routing mechanism, and Yamashita et al.’s (2005) work on a centralized route information sharing system. This section also presents work of Biddlestone et al. (2011) on vehicle ad hoc group formation. Claes et al. (2011) introduced a distributed approach for anticipatory vehicle routing that is particularly useful in a large scale dynamic environment. The approach is based on Delegate Multiagent Systems, i.e. an environment-centric coordination mechanism that is in part inspired by ant behavior. Vehicles behave like ants; instead of depositing pheromones, they deposit information at a road side unit. The system is made up of vehicle agents, infrastructure agents, and a virtual network. The information gathered from intention ants is used by road agents to predict future travel time on links in the network. Simulation results showed that forecast information lead to shorter trip times. Use of external TMC data and routing strategies based on the distributed information drastically
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FINAL REPORT outperformed standalone route guidance strategies. MAS worked best under both dynamic and static input rates, finding both the shortest route and the fastest route. The distributed nature of this approach fits the distributed nature of the traffic domain and ensures that scalability requirements can more easily be met than in centralized systems. The simulation experiments conducted by the authors showed that the use of forecast data gathered in a distributed manner could help drivers reach their destination up to 35% faster than drivers with no data or only realtime data made available by TMC services. Du et al. (2014) proposed a distributed coordinated in-vehicle navigation system using a mixed strategy congestion game (modeling route choice behaviour of drivers), which outperforms traditional routing based on user equilibrium both in system cost and average cost. The coordinated online in-vehicle routing mechanism (CRM) is based on vehicle to vehicle communication without a need for central route information server. In their problem, a large number of smart (equipped) vehicles are present in a network and the same short time period. The routing decision is made by equipped vehicles first determining the priorities of candidate routes and then picking a route based on the priorities. These vehicles then form an ad hoc group according to recent routing priorities where each vehicle acts as a player trying to choose a route with minimum expected travel time. This is considered tactical group formation. For more information on tactical group formation and other forms of vehicle ad hoc group formation the interested reader is referred to: Biddlestone et al (2011), Chen and Cai (2005), and Zhang et al. (2011). Unlike Claes et al. (2011) and Du et al. (2014), Yamashita et al (2005) proposed a route information sharing where travelers share their route information with central route information server. The route information server collects all route information from all RIS drivers and aggregates it per link. The aggregation in RIS is done using a passage weight. This passage weight is used to take into account the uncertainty of a vehicle actually passing on the link. For more information on how the passage weight time is calculated please refer to Yamashita et al. (2005). Figure 19 presents outline of route information sharing for the case of having centralized route information server.
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Figure 19. Outline of Route Information Sharing (source: Yamashita et al, 2005)
4.2.
Distributed Anticipatory Route Guidance Framework
We propose a framework for implementing a distributed anticipatory route guidance using Mirasan as a central route information server. Figure 20 presents the key components of the designed framework. As shown in Figure 20 the main components are: ad hoc vehicle group formation, information sharing and travel time prediction. Each component is described in the following sections. 4.2.1. Ad hoc vehicle group formation While en-route, those equipped vehicles who are about to make route choice decision using V2V communication (DSRC) will communicate with each other and form an ad hoc group (similar to Figure 18) with a leader. This group formation is similar but different from the one proposed by Du et al. (2014). In their study, Du et al (2014) proposed that equipped vehicles in the network form a group whenever they arrive at an intersection to make a route decision. In reality, however, this will not be feasible because only those vehicles that share the same route will affect each other’s route choices so there is no need for information sharing among all vehicles. In addition, in a wide network it is not possible for all equipped vehicles to communicate with each other due to the restriction on communication range and latency. For better understanding 25
FINAL REPORT of vehicular communication systems, the interested reader is referred to Papadimitratos et al. (2009), Yang and Wang (2007), and Popescu-Zeletin et al. (2010). In order to take advantage of short communication range, reduce the number of groups formed, and increase the efficiency of formed groups, we propose using waypoints in such a way that vehicles passing through the same waypoint will form a group at a decision point upstream of the waypoint. Start At intersection D Using traffic App, each vehicle nB, nE and nD will get kshortest path to their destinations (B, E & F) using current travel time Using DSRC equipped vehicles will communicate with each other to check if they will pass the same way-point to their destination
Form vehicle ad hoc group and designate a leader
Coordinated Routing- Information Sharing
Member of the group inform the leader of their intended route choice
Leader informs the Route Information Server of the choices of the member of the group using the App
Central Route Information Server evaluates traffic based on route choices of the group and sends back updated travel times using the App
Group leader informs the member of the group of the new travel times over DSRC
Members of the group revise their route choice and inform the leader over DSRC
Equilibrium Route Choice
No
Yes End
Figure 20. Distributed anticipatory route guidance based on ad hoc group formation.
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FINAL REPORT As defined by Zuurbier (2010), a waypoint is a large infrastructure junction located downstream of the decision point that must be passed to reach the intended destination. Waypoints are landmark structures already known to the general public. The waypoint functions as a merge point of alternative routes that can be accessed from the decision point. While en-route, equipped vehicles using their traffic app first to find k-shortest paths to their destination. Depending on the algorithm used by Mirasan, the k-shortest paths can be based on historical travel time, real time, or predicted travel time. Once equipped vehicles become aware of their shortest paths to their destinations, they communicate with other equipped vehicles around them at the decision point to find out if they share a waypoint with them. Those equipped vehicles that pass through the same waypoint to get to their destination will form an ad hoc group with a leader. Selecting a leader reduces communication frequency with the central server. For more information on information dissemination between vehicles an interested reader is referred to Sanguesa et al. (2014). 4.2.2. Information sharing Similar to Yamashita (2005), we chose communication with the central route information server as opposed to communication with infrastructure agents (Claes et al., 2014) or pure V2V communication (Du et al., 2014), since Transnomis already has a central route information server, Mirasan. There are in general two different types of information sharing in the proposed framework, namely: information sharing between members of the group (via dedicated short range communications (DSRC)) and information sharing between the leader of the group and the central route information server (via the app). Once a group is formed, individual members of the group inform the leader of their chosen route via DSRC. The leader then communicates route intentions of the group members to the central route information server via the developed App requesting the updated travel time for kshortest path from current decision point to the next waypoint. The centralized route information server provides the leader the anticipated travel time based on the choices of the group members. The leader then provides the server with the final intentions of the member of the group and the server uses this to update anticipated travel time on the links for future use. At each instant, different groups in the network can be formed and can communicate with the central information 27
FINAL REPORT sever. Therefore, the central sever has full view of the network and keeps the choices of other groups into account when calculating anticipated travel times for the paths. 4.2.3. Travel Time Prediction from Distributed Information In this scheme, the centralized route information server determines anticipated travel time based on the distributed information provided by choices of different group members and vehicles. It calculates anticipated travel times on those k-shortest paths using intention information received from other vehicles and the group members. One of the main challenges associated with distributed cooperative anticipatory route guidance is that global information is not available and route travel time prediction accuracy depends on the number of vehicles that are equipped with the navigation device and willing to share their route information. Claes (2015) proposed using an artificial neural network (ANN) for ad hoc link traversal time prediction in case of distributed information system. Figure 21 presents a simplified layout of ANN proposed by Claes (2015). The actual layout used had 1 input layer with 8 input neurons, 1 hidden layer with 17 hidden neurons and 1 output layer with 1 output neuron.
Figure 21. Artificial Neural Network Layout for Link Traversal Time Prediction (Claes, 2015)
When calculating the link traversal time at time 𝑡, the inputs for the ANN are defined as follows: 28
FINAL REPORT 𝑖𝑛𝑝𝑢𝑡0 (𝑡) = 𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛_𝑙𝑒𝑣𝑒𝑙(𝑡) 𝑖𝑛𝑝𝑢𝑡𝑖 (𝑡) = 𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛_𝑙𝑒𝑣𝑒𝑙(𝑡 − 𝛿 𝑖−1 ) Where: 𝑖𝑛𝑝𝑢𝑡𝑖 (𝑡): the input for ith input neuron 𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛_𝑙𝑒𝑣𝑒𝑙(𝑡) : the intention level currently stored for the future time 𝑡, i.e. the mean number of vehicles that are going to use link (𝑙) at time (𝑡). Output: the predicted link traversal time based on actual number of vehicles that are going to be on link (𝑙) on time (𝑡). The neural network presented in Figure 21 only takes into account the effect of number of vehicles on current link on travel time. However, it is possible to also incorporate the effect of traffic on upstream and downstream links on the travel time of the link as well. For example, in an experiment Claes (2015) incorporated the capacity of the downstream link in the network by adding additional input neurons. Another option would be to include uncertainty to the forecasted travel time. The ANN model presented here differs from other link traversal time prediction methods as it is a distributed approach that relies on mechanistic properties of traffic flow in a network to construct a picture of the near term future. The ANN model predicts future link travel times based on the route information of the user shared with central route information server. The new travel time prediction model using ANN requires the use of connected vehicles, whereas the methods introduced in section 3 can be implemented without a need for connected vehicles or route information sharing between vehicles and a central route information server. Even though the proposed ANN method will allow the central route information server to predict future travel time based on limited information from the equipped server, there are some challenges with using this method which should be taken into consideration. Claes (2015) used traffic data from the city of Leuven based on a calibrated traffic model. In addition, Claes (2015) implemented distributed route guidance based on V2I. For each link, they trained a link-specific ANN model for the road side unit associated with that link.
4.3.
Non-Technical Issues & Acceptance
In the case of V2V communication and V2I communication, there are issues of security (computer hackers can target intelligent/automated vehicles and cause traffic disruptions) and privacy (who should own or control the vehicle’s data, what type of data will be stored? With 29
FINAL REPORT whom will these data sets be shared?). There is a need for consideration of security and privacy when it comes to information exchange (Dwork, 2008; Kargl et al., 2013; Bansal et al., 2011). In addition, an important factor that has major effect on the effectiveness of cooperative route guidance is providing sufficient incentives for drivers to be willing to cooperate and form coalitions, as mentioned in the beginning of Section 4.
4.4.
Route Guidance app
A route guidance app is designed to work with the MATLAB program simulating the central route information server. Figure 22(a) is the main interface of the mobile app for cooperative traffic navigation. There are five main menus on the interface: Origin, Destination, Upload, Request and Select. The Origin and Destination menus are used to interactively set up the start and end points on a map. The green point is the Origin, and the red point is the destination. Once the origin and destination are setup, users can click on Upload menu to upload the longitudes and latitude to the MATLAB program. The MATLAB program calculates the k-shortest paths that are displayed in different colors in Figure 22(b). Yen’s (1971) algorithm, developed on a freely distributed code online, was used for this purpose. After that, users can click the Request menu on the app to read paths and draw them on the map as shown in three different colors (yellow, red and blue). The users are supposed to select one of them and share the information on the server. As shown in Figure 23(b), the selected path (vehicle intention) will be uploaded to the server and used in the MATLAB program for travel time prediction.
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(a) (b) Figure 22. (a) Interface for route guidance, and (b) k-shortest paths in MATLAB program
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(a) (b) Figure 23. (a) Main interface, and (b) results of coordinated route guidance on App
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5. Conclusion This project proposed a connected and autonomous vehicle (CV/AV) architecture that aims to act as a surrogate system to cost-effectively evaluate transportation technologies for CV/AV. The surrogate system can mimic logical architecture processes to reduce costs of ITS deployment. Specifically, (1) we selected two travel time prediction models, e.g., ARIMA and OL-SVR models, and implemented the algorithms as server side programs to predict recurrent and nonrecurrent travel times using path-based floating car data; (2) we designed a cooperative anticipatory route guidance framework that can be tested and implemented by Transnomis in the future to help drivers coordinate their route choice and reduce congestion; (3) we developed two Android-based mobiles apps for collecting real time travel time data from floating cars to be used for travel time prediction, and for distributed anticipatory route guidance between equipped vehicles and a central route information sever (Mirasan). The above developed programs are built on mobile devices to mimic and evaluate CV/AV technologies such as on-board devices and DSRC devices.
5.1.
Deliverables
The following items listed in Table 2 were delivered to Transnomis at the close of this project. Table 2. Deliverables provided to Transnomis No. 1. 2. 3. 4. 5. 6. 7.
Filename Monthly meeting notes TransTime.rar TransCoRouting.rar arima.dll olsvr.jar Test_ARIMA_OLSVR.zip CooperativeAnticipatoryRo● uteGuidanceSimulation.zip ● ● ● ●
8.
CARS.pdf
Description files shared via Google Drive Codes of mobile app for retrieving predicted travel time Codes of mobile app for distributed route guidance C# DLL of ARIMA model, notes are included JAR of OL-SVR model, notes are included An example code file for the usage of each model Executable MATLAB codes for simulating functionality of central route information server to be used with or without the mobile app (CooperativeAnticipatoryRouteGuidanceSimulation.exe) MATLAB run time (MyAppInstaller_mcr.exe) Input/Output folders Instruction manual (InstructionManual.pdf) list of OD pairs for testing the program without the mobile app (ODlist.txt) MATLAB codes script file, notes are included.
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5.2.
Future directions
There were a series of challenges in this project. First was the selection of short-term travel prediction model using path based floating car data, which has seldom been touched before. We considered efficiency and usability for Transnomis. To make it usable for Transnomis platform, we carefully analyzed the existing system of Transnomis and customized the development accordingly. Second, the direct communication among drivers when they share cooperative routing information. We developed asynchronous web services for drivers to share route choice information to server and then actively send information to other drivers. In this way, we are able to mimic the real time communication among drivers. A number of directions can be taken in future research. In this project, path travel times were used to develop short travel time prediction models. The limitation of using path travel time is that the server needs to enumerate all the possible paths between origin and destinations, which is not feasible for a large network. Future research should focus on short term travel time prediction using floating car using link travel times. However since links differ from each other, it won’t be feasible to have only one model for the entire network. Therefore categorical models should be developed for different link types, such as freeways, arterial, local. In this study, floating car travel time data from were obtained from TTC buses. In the future it may be possible to use data collected from connected vehicles. Furthermore, a simulation study can be conducted to compare the performance of distributed travel time prediction using V2V and travel time prediction using centralized server and floating cars. In this study, the framework for the distributed anticipatory route guidance was designed, but not tested. Future research should test the performance of the proposed framework using simulation and field tests. The current developed app for the route guidance only mimics the communication between vehicles and server (back and forth). It would be nice to also add a feature that mimics the V2V communication and ad hoc vehicle group formation. The developed app for the travel time prediction, currently only collects current position, time, and speed of the vehicle. Future work should try to collect more information from the vehicle, such as video information, and information from the OBD.
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FINAL REPORT The framework proposed in this study for the distributed anticipatory route guidance assumed that equipped vehicles are willing to share information with each other. Future work should look at mechanism design for cooperation among drivers to predict when they would share information and with whom.
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