Traffic Monitoring for Coordinated Traffic Management

2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) October 8-11, 2014. Qingdao, China

Traffic monitoring for coordinated traffic management—experiences from the field trial integrated traffic management in Amsterdam Hans van Lint1 , Ramon Landman1 , Yufei Yuan1 , Chris van Hinsbergen2 and Serge Hoogendoorn1 considering all urban arterials connected to the A10-W in phase 2, and considering the entire network in phase 3. Note that in phases 2 and 3, the roadside measures will be integrated with in-car measures as well.

Abstract— In the first phase of the large-scale field trial integrated traffic management in Amsterdam (acronym: PPA), a hierarchical control approach is used to coordinate the algorithms of the ramp metering installations along a densely used freeway corridor with the intersection controllers along one of the connecting urban arterials. This hierarchical control approach requires a similarly organised hierarchical monitoring approach. This approach is based on several freeway and urban state estimation and prediction techniques. In this contribution we describe and discuss two of these in terms of their underlying rationale and we give some some examples of their application. In the final part of this paper we discuss a number of critical issues that came up in the implementation of these methods in the PPA. These relate to the difficulty of communicating the underlying scientific ideas into practice. They also relate to the fact that some of the underlying (scientific) puzzles have not yet been solved.

I. I NTRODUCTION AND CONTEXT The Praktijkproef Amsterdam (Field Operational Test Integrated Network Management Amsterdam) – or PPA for short – aims at gaining practical experience with applying INM (Integrated Network Management) in a large-scale regional (urban and freeway) network. INM aims to improve the effectiveness of deploying traffic management measures in an integrated and coordinated way. Although INM is not new, practical experience by means of Field Operational Tests (FOTs) is lacking. Many of the approaches proposed in literature are based on optimal control and MPC (e.g. [1], [2], [3], [4], [5]). Although superior in terms of flexibility and robustness, for practical applications the computational complexity, and lack of transparency, are sufficiently serious disadvantages to consider heuristic but still generic approaches. Within the PPA project, the national road authority Rijkswaterstaat has opted for a stepwise implementation of the network control approach. In the first phase of the project, we therefore focus on a freeway arterial (the A10-West) and its on- and off-ramps, and one connecting urban arterial (the s102). Fig. 1 shows the considered arterials. Note that the s102 is fully equipped with intersection controllers, and that all on-ramps on the freeway arterial are equipped with ramp metering installations. In the subsequent phases, larger areas of the Amsterdam network will be considered, starting with

Fig. 1. Overview of considered network for phase 1: T1 (urban arterial, the s102), T2 (freeway arterial, the A10-W) and T3 (all arterials).

The hierarchical control rationale within the PPA is based on the following principles: 1) To prevent the capacity drop at the freeway. 2) To prevent spill back at the freeway and urban arterials. 3) To use spare storage capacity in the network given the prevailing Level of Service (LoS) of the subnetwork and the archetype situation (traffic problem) that requires a specific control action. 4) To solve problems locally if possible, and to coordinate (escalate)only when needed. The control architecture and the governing control laws for ramp metering, and coordination at the three levels considered (freeway ramps, intersections and the combination of these two—see Fig. 1) have been described in detail in [6]. The good news is that, since the publication of [6] early 2014, the PPA control system has been successfully evaluated and is actually working in practice from May 2014 onwards. We will return to some of the lessons learned in the months just before the switch was turned on definitively in section V. In this contribution we focus on the monitoring components (state estimation and prediction) in the PPA.

1 Hans van Lint [email protected], Ramon Landman [email protected], Yufei Yuan [email protected] and Serge Hoogendoorn [email protected] are all with the Transport & Planning Department, Faculty of Civile Engineering and Geosciences, Delft University of Technology, Stevinweg 1, Delft, The Netherlands 2 Chris van Hinsbergen is with Fileradar B.V. [email protected]

978-1-4799-6077-4/14/$31.00 ©2014 IEEE

477

Parameter   es*mator  

Freeway  state   es*mator   LoS  indicator   Queue  es*mator  /   predictor  

Freeway  bo>leneck   Inspector  

Storage  indicator  

Urban  bo>leneck   Inspector   Hierarchical   control  scheme   PPA  

Fig. 2. Simplified scheme of the monitoring components in the hierarchical PPA control system. Fig. 3. Definition of the network level-of-service based on the average density and the spatial variance in the density.

II. T RAFFIC MONITORING IN THE LARGE SCALE FIELD TRIAL TRAFFIC MANAGEMENT A MSTERDAM Input : Queue lengths and maximum buffer lengths. Output: Storage space per buffer.

Fig. 2 provides a simplified scheme of the monitoring components in the hierarchical PPA control system. Before discussing the two core monitoring components (grey in Fig. 2) in more detail we briefly overview each component, in terms of its central objective, the input it requires and the output it produces.

D. Level of service indicator Goal : Making subnetwork state estimates based on a “3D” generalisation of the network fundamental diagram [7]. This estimate infers the production (throughput or exit rate) as a function of density and the standard deviation of density. The example in Fig. 3 illustrates this indicator. Input : Speed, flow and density contours freeway and queues in urban buffers. Output: LoS indication (Fig. 3 illustrates these levels with “iso-LoS” lines that separate good (LoS GREEN) conditions from medium (YELLOW), dense (RED) and bad (BLACK) conditions).

A. Freeway state estimator Goal : Making continuous and unbiased freeway state estimates in terms of speed, flow and density. Input : Raw freeway loop detector data (flows and speeds). Output: Speed, flow and density contours. We will discuss this component in section III B. Urban queue estimator / predictor Goal : Estimating queue lengths in urban buffers (i.e. designated intersection approaches). Input : Raw urban loop detection data and data from the intersection controllers (green times, etc). Output: Queue lengths and traffic demand in buffers. The combination of these also enable depending components to infer predictions of the queue length., We will discuss this component in section IV

E. Parameter estimator Goal : Estimating the critical density and the capacity of freeway bottleneck locations. To this end, the Kalman filtering approach described in [8] is used. Critical density is the key parameter in the ALINEA ramp metering controllers [9] and the capacities are used for example (in conjunction with densities) to establish service levels in the LoS indicator. Input : Speed, flow and density contours freeway. Output: Critical density per road segment.

C. Storage space indicator Goal : Determining storage space in buffers on the urban network; depending on the characteristics of the traffic problems (archetype bottleneck situation— described in [6]) and the present level of service of the subnetwork this buffer belongs to. For example, in case of a bottleneck (over-saturated intersection, or blockade) on the urban arterial that is not hindering traffic on the freeway, the general control idea is to buffer traffic into the direction of the blockade on locations where other traffic (i.e. not having to pass the bottleneck location) is not hindered.

F. Freeway bottleneck Inspector Goal : Anticipating freeway breakdowns. Based on the freeway state estimates, the freeway bottleneck inspector (FBI) determines the (predicted) location of a freeway bottleneck. This is achieved by first determining so-called hot-zones (locations where the breakdown probabilities are high) using historic data, and combining this with prevailing speed estimates from the Freeway state estimator. 478

Input : Speed, flow and density contours Output: Breakdown probabilities for so-called hotzones and/or—if congestion has already set in—head, body, tail location of congestion.

Configurable   defaults  &   params  

G. Urban bottleneck Inspector Goal : Recognizing severe over-saturation or spill back in urban buffers and labelling these as bottleneck. The component compares the current queue lengths to the allowed buffers and returns the difference. Note that the buffers, describing the locations and lengths of the queues that are allowed, given the prevailing network level-of-service, have been determined in close collaboration with the municipality of Amsterdam Input : Queue lengths and maximum queue threshold to mark a buffer as bottleneck. Output: Buffers labeled as bottleneck.

Raw speeds & flows

Adap)ve   Smoothing   (ASM)  

(Offline)  wave   speed  /  param   es)mator  

Density   es)ma)on  

Speed  correc)on   method  

Speed, flow & density contours

III. F REEWAY TRAFFIC STATE ESTIMATION

Fig. 4. Schematic overview of the freeway state estimator implemented in the Amsterdam field trial.

Traffic state estimation methods can be classified in many different ways. They differ in terms of which data are used, e.g. point-based data (from loops); trajectory data (floating car data—FCD), travel time data, or a combination of these. They also differ in terms of the underlying (traffic flow) assumptions and theories that are used. In some cases these are simple relationships (travel time equals distance over speed), e.g. [10]; in other cases entire traffic flow simulation models are used, e.g. [11], [12]. Although complex approaches offer more flexibility and a wider application domain, they also demand more elaborate methods to tweak and tune their parameters. In many cases the available data do not warrant all these additional assumptions. Another distinction relates to the data assimilation methods used to combine (assimilate) the available data and the model predictions. Also here, there is a wide range of possibilities, ranging from simple aggregation and smoothing methods [13] to Kalman filters [11] and more involved dynamic Monte Carlo sampling methods, and all these have their own pros and cons. In model-based control approaches (e.g. Wang and Papageorgiou’s RENAISSANCE [11] and recent work in our own lab [12]) macroscopic traffic flow models are used in conjunction with an (extended) Kalman filter to estimate the state (densities, speeds) in a traffic network based on mall available sensor data. Although there are many benefits of this approach over other approaches—most importantly it provides an integral network approach to estimation, prediction and control optimisation; in the PPA phase 1 we chose a simpler approach to freeway state estimation for mostly pragmatic reasons. In section V we briefly discuss these. Fig. 4 schematically overviews this combination of methods (e.g. [14], [15], [13], [16]), which together are named the “Fileschatter” (Dutch for “congestion estimator”). The overall idea of this state estimator is schematically given in Fig. 4. Based on historic (raw) loopdata the two dominant kinematic wave speeds (cf ree in free-flowing traffic, and ccong for congested traffic) are deduced using

the image processing methods in [15]. These wave speeds determine how information (e.g. a disturbance) propagates through a traffic stream, e.g. in congestion disturbances travel against the flow direction with typically around ccong = −18 km/h. Below we briefly discuss the three main components in Fig. 4: speed-correction; adaptive smoothing and density estimation. A. Methods to correct the bias in speeds due to local averaging Local (arithmatically) averaged speeds uT provide biased approximations of the space mean speed uS , for reasons explained in many traffic flow textbooks. This bias is proportional to speed variance σS2 and inversely proportional to average speed, that is uT = uS +

σS2 uS

(1)

In other words, the lower the average speed and the more inhomogeneous speeds are distributed over space, the larger the error due to time averaging becomes. Since the continuity equation q = ρu assumes homogeneous and stationary traffic conditions, computing density with ρ = uT /q leads to huge errors between 25-100% [17]. The direct solution to solve this problem is to compute harmonic speeds at detectors uS ≈ 

1 N

1 P

1 ui

;

(2)

however, in practice updating the internal logic of road side sensors is a costly operation. The second best choice is to correct the speeds afterwards. In the PPA pilot we implemented the speed-bias correction method described in [14] for carriageway data, which is schematically explained 479

(a) Correction principle

q

q/u (corrected) q/u* (measured)

cfree correc%on  

kc

ccong k

(b) Validation with simulated data Reference data Free-flow data Congested-flow data Corrected data

Fig. 6.

Example densities estimated along the A10 West.

in which P zβ (t, x) = Fig. 5. Speed-bias correction algorithm for carriageway data: (a) the underlying principle; (b) validation using data from a microsimulator FOSIM [18]

i

φβ (t − ti , x − xi )z obs (ti , xi ) P i φβ (t − ti , x − xi )

with β ∈ {free, cong}, and φβ a kernel function   |t| |x − t/cβ | φβ (t, x) = exp − − τ σ

in Fig. 5. This method uses the fundamental diagram (parameterised by the aforementioned wave speeds and the capacity and critical density estimates from the “Parameter estimator”) to correct the raw data. For lane-based speed-data a simpler speed-correction algorithm is applied. Let u`T denote the lane (arithmetic) mean speed and q ` the number of vehicles passing. Under the assumption that the differences between lane speeds constitute the largest part of speed variance, carriageway average speeds are computed as the harmonic average of the lane average speeds. P ` q uS ≈ P `  ` (3) q uT

(5)

(6)

In the ASM the time axis is skewed along the two dominant wave speeds cf ree and ccong . The estimate z(t, x) is thus a weighted average of the available observations z obs (ti , xi ), with weights that decay (along the dominant wave directions) as observations are further away or measured in periods farther back (or forward) in time. By choosing z = 1/u as state variable, the ASM provides a direct means to compute the (exponential) harmonic average of observed speeds (see e.g. [19]). The weight α used in equation (4) to combine the free flow and congested estimate, finally, is determined as a function of the minimum speed (maximum pace) reconstructed with equation (5). Treiber and Helbing [16] propose the following expression for this weight factor:    1 vc − v ∗ (t, x) α(t, x) = 1 + tanh (7) 2 ∆v

B. Density estimation

with:

With the corrected carriageway speeds and flows, now (carriageway-average) density is estimated as follows. First for every time and location {t, x} of interest (usually a equidistant grid with distances δx ∼ 200m and periods δx ∼ 30s) the average flow q [veh/h] and the “pace” ω = 1/u [h/km] is computed using the adaptive smoothing method (ASM), described in [16], [13]. Before we explain why we choose to compute ω instead of u, first a very brief explanation of the ASM. The central idea of the ASM is that the traffic state z(t, x) for any {t, x} can be computed as a convex combination of two estimators, zf ree (t, x) for free-flowing traffic, and zcong (t, x) for congested traffic respectively:

v ∗ (t, x) = min (ucong (t, x), uf ree (t, x))

(8)

in which vc depicts a so-called critical speed (discriminating between congestion and free-flowing traffic), and ∆v determines the ”bandwidth” around this critical speed. The final step then is to compute the state variables on the basis of the ASM outputs. These are carriageway flow q, speed u = 1/ω and density ρ = qω. The “Fileschatter” system outlined here was validated in the FOSIM micro simulation environment [18] under various scenarios. Clearly, since on simulated data the speedbias correction procedure is highly effective (Fig. 5), the estimation of density from these corrected and virtually bias-free speeds in the same simulated experiments is very accurate, even in cases with several detectors missing and

z(t, x) = α(t, x)zcong (t, x) + (1 − α(t, x))zf ree (t, x) (4) 480

added noise to the detector output. Earlier work has shown the robustness of the ASM method with respect to missing data [13], [19]. At this point we can only provide some qualitative remarks about the validity of the state estimator in practice (since no ground-truth densities are available). Fig 6 shows a density contour along the 14 km A10 West. Most importantly, the density values around congestion are plausible (in the range 18-25 veh/lane/km) as well as the estimated critical density values. An extensive validation of the PPA coordination scheme is carried out at the time of writing. The results of that validation will disclose in more detail how well the state estimator works under different circumstances. IV. U RBAN QUEUE ESTIMATION Computing the amount of vehicles present or queuing at an intersection requires, of course, counting the number of vehicles that enter or exit the queuing area. Given a closed set of sensors (usually loopdetectors) that count all vehicles entering and leaving the intersection, the simplest (and exact) approach to compute the number of vehicles N (k) at discrete time steps k of size ∆t) reads: N (k + 1) = N (k) + ∆t(q in (k) − q out (k))

Fig. 7. Example of the drift-error in computing nr vehicles in a queue based on erroneous flowcounts

(9)

Unfortunately, in case q in and/or q out contain errors—and this is always the case—this error accumulates over time and the estimate in equation (9) becomes a random walk. In other words, the number of vehicles in the queue and hence the queue length becomes fundamentally unpredictable as illustrated in Fig. 7. There are several alternative queue estimation methods available in the literature (e.g. [20], [21]), which all make use of additional or other sensor data (e.g. speed, occupancy) to infer the queue length. In the PPA project a new occupancy based queue estimator has been implemented. Without going into detail, the estimator uses the time-occupancies from the available loop detectors upstream of each intersection to infer the most probable queue-length. These “long” loops are placed at one or more locations upstream of each intersection typically 100 or 200 meters apart. The algorithm is a four-step process. • First, the algorithm converts the time-ocupancies into space-occupancies using [22]. • Next, the algorithm determines the most upstream detector di with a spatial occupancy higher than a critical value oc . This provides the lowerbound for the queue length. Note that this critical occupancy has been determined by combining video footage (identifying the actual queue length) with the corresponding dataset of occupancy. • The third step is to determine the upperbound for the queue length, the detector upstream of di that has a space occupancy lower than oc . • The actual queue length is then inferred from these values and the local geometry. This queue estimator was validated first using microsimulation (VISSIM) data and secondly against real data (video footage). Fig 8 shows the typical performance of this queue

Fig. 8.

Performance of the PPA queue estimator against real data.

estimator on one of the buffers for which several days of video footage was available. The horizontal lines in Fig 8 depict the occupancy signals from the detectors at the locations (in m) indicated on the vertical axis. The results show that in this case, the predicted queue length follows the actual queue length quite well albeit with a slight lag. Most importantly, in most cases the queue tales were succesfully mapped between the “correct” detectors. V. D ISCUSSION AND O UTLOOK The overview of components in section II highlighted seven functional monitoring components (also dubbed logical monitoring units) developped for the PPA, of which we briefly discussed two in this paper. If we count all monitoring, supervision and control components, the number quickly exceeds twenty. And this number of functional components is dwarfed by the number of actual software routines implemented and now operational in the PPA. One of the biggest challenges was that the tendering procedure for the implementation required a complete functional specification for all these components (including state estimator, queue estimator, etc) which would then be translated in technical specs by the ICT department of the highway administration, on the basis of which industrial partners would then implement these in their existing systems (or in new ones). There are many arguments against this “waterfall” model for system development in large-scale innovation 481

projects, and in this light the projectleaders did a miraculous job in succeeding to actually implementing it. Even moreso considering the fact that an entire new control logic has been developed with enough innovation for a PhD thesis and many articles. There are many lessons from this project to be learnt, also related to traffic state estimation. In many road administrations the design and maintenance of monitoring systems is the responsibility (and the prerogative) of the ICT department of that organisation, which in some cases outsource part of their tasks to private contractors. This makes sense, given the hardware (sensors, communication, servers, etc) and software (communication, processing, storage and retrieval) involved. However, the fundamental design questions, related to where (do we need to place detectors), what (quantities should they measure and in which quality) and how (must we process, aggregate and (f)use) the data, must clearly come from traffic and control engineers. In practice, however, it appeared that many of these ”monitoring” choices were not made to serve traffic control or management needs. The PPA in that sense was a strong wake up call for much more collaboration between the departments of road authorities The need for a speed-bias correction module, for example, is the direct consequence of the decision over twenty years ago to equip double loop detectors on Dutch freeways with embedded routines that compute time averaged speeds, an error that only has only surfaced in the last 10 years as researchers began to use these archived data. A second and related lesson is that the importance of accurate queue estimation in urban regions is highly underestimated in practice. There is a minimum accuracy required in the estimation of queues beyond which integrated / coordinated control is no longer possible and meaningful. This requires the right algorithms and methods, but it does not always imply more sensors. The requirements are sufficient sensors at the right place measuring the right information, combined with methods and algorithms based on traffic flow theory.

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