Research and Implementation of Lane-Changing Model Based on ...

Research and Implementation of Lane-Changing Model Based on Driver Behavior Daniel (Jian) Sun and Lily Elefteriadou The purpose of this research was to collect data related to driver behavior to develop a new lane-changing modeling framework for urban arterials. The specific objectives were to

Lane-changing algorithms have attracted increased attention during recent years. However, limited research has been conducted to address the probability of changing lanes and vehicle interactions that occur. The objective of this study is to model urban lane-changing maneuvers by using data related to driver behavior. Two components were developed from field data, for lane-changing probability and for gap acceptance. Two experiments were conducted to collect the corresponding lanechanging data: a focus group study and an in-vehicle driving test. The proposed lane-changing model was implemented in the CORSIM microscopic simulator. Traffic data collected from a busy arterial street were used for model calibration and validation, and the simulation capabilities of the newly developed model were compared with the original lanechanging model embedded in CORSIM. Results indicate that the new model replicates observed traffic under different levels of congestion better than the original model does.

1. Collect traffic data related to driver behavior, especially lanechanging behavior, on arterial streets; 2. Develop a new framework for modeling lane-changing maneuvers in an urban street environment; and 3. Implement the lane-changing model in CORSIM and evaluate its performance. The remainder of the paper is structured as follows. First, the literature related to lane-changing models is summarized. Then, the framework for modeling lane changes is presented, along with related data-collection and analysis efforts. Next, model implementation, calibration, and validation in CORSIM are described. Finally, conclusions and recommendations for future work are presented.

The lane-changing maneuver is a fundamental driver behavior that determines vehicle distribution across lanes. However, limited research has been reported regarding lane changes on urban arterials, where occurrences are more frequent (1, 2). Only a few researchers have tried to study the maneuvers under congested conditions (3, 4), and even these models haven’t incorporated vehicle interactions or variability in driver behavior. Research documenting the drivers’ thinking processes, as well as support for assumptions used in existing models, is scarce. The major reason for this scarcity is the lack of reliable data related to driver behavior (5, 6). The data required to model lanechanging maneuvers include the position, speed, acceleration, and length of not only the subject vehicle but also vehicles ahead of and behind the subject vehicle in the current and adjacent lanes. Additionally, site-specific factors such as speed limit and geometry of the subject road segment affect lane-changing behavior. Historical data from cross-sectional detectors are insufficient to understand these factors. Only with the wide use of video devices and the emergence of video-tracking tools over the past 10 years have traffic engineers been able to collect the high-quality vehicle trajectory data that capture lane-changing maneuvers in detail (7 ).

LITERATURE REVIEW SUMMARY Selected microscopic lane-changing models are reviewed. Other research related to lane-changing behavior has been conducted—the multiregime traffic simulation model (8), the heuristic structured lane-changing model (3), and the politeness-based model (9)—but is not reviewed here because of space limitations. Gipps’ model is one of the earliest rule-based lane-changing models (10). By connecting lane-changing decisions to urban driving situations, Gipps’ model incorporates important factors such as existence of a safety gap, location of permanent obstructions, intent of turning movement, presence of heavy vehicles, and speed advantage. From these criteria, subject drivers decide whether to move into the target lane. The lane-changing process can be summarized as a decision tree with a series of fixed conditions in which situations that may be encountered on urban arterials are considered. The decision to change lanes is a rule-based event, and the output is a binary choice (change or not change). The overall structure is flexible, and any new or special reasons for changing lanes can be added or replaced. However, this model does not consider variability in individual driver behavior, particularly the deterministic rule priority system that ignores trade-offs among considerations. In terms of model validation, no rigorous methods were proposed for estimating model parameters in the testing platform, MULTISIM. On the basis of field data collected in the central business district of Sydney, Australia, Hidas categorized three types of lane changes— free, cooperative, and forced—depending on traffic conditions (1, 4). The autonomous agent technique was used to model driver responses

Transportation Research Center, Department of Civil and Coastal Engineering, University of Florida, P.O. Box 116580, Gainesville, FL 32611-6580. Current affiliation of D. Sun: School of Transportation Engineering, TongJi University, 4800 Cao’An Road, Jia-Ding District, Shanghai 201804, China. Corresponding author: D. J. Sun, [email protected]. Transportation Research Record: Journal of the Transportation Research Board, No. 2161, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 1–10. DOI: 10.3141/2161-01

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and interactions that occurred during maneuvers. If one driver perceives that another driver intends to merge into its lane, actions may be to give way, slow down, or not give way, depending on the congestion level and individual driver characteristics. In ARTEMiS, the microscopic traffic simulator developed as the testing platform for implementing the lane-changing model, all existing vehicles are checked sequentially with respect to their intention to change lanes. One disadvantage is that only the lag vehicle has the ability to start a cooperative lane change. When a free lane change is impossible, if the subject vehicle is checked first, then a forced lane change is brought about. Otherwise, if the follower is checked first, then it will provide courtesy and the situation becomes cooperative. Consequently, the passive or active deceleration of the lag vehicle, which determines lane-changing mode, depends exclusively on the checking sequence of the vehicles because the model does not consider vehicle communication. Choudhury et al. proposed a combined merging model, including courtesy and forced merging for mandatory lane changes (MLCs) (11). This model focuses on freeway operations and, like the Hidas model, consists of three modes: normal, courtesy, and forced. The anticipated lead and lag gaps are approximated by incorporating the courtesy deceleration from the lag vehicle if a normal lane change is impossible. If the anticipated gaps are still unacceptable, then the subject vehicle will consider forced merging for MLCs, wherein the driver anticipates the deceleration that the lag vehicle would apply to avoid collision. Sensor data and aggregate trajectory data collected from a highly congested section of I-80 in Emeryville and Berkeley, Calif., were

used for model implementation and validation. MITSIMLab was used to simulate and calibrate the lane-changing model (12). Simulation test results indicated that forced and courtesy maneuvers had a significant impact on the macroscopic results of highway traffic flow. However, this model considers only freeway traffic and is not applicable to urban lane-changing maneuvers. In summary, with respect to lane-changing maneuvers on urban streets, driver characteristics and perceptions have not been incorporated into lane-changing models. Furthermore, existing lane-changing models cannot accurately replicate lane changes on urban arterials. Especially under congested conditions, lane changes on arterial streets are affected by various driver interactions and characteristics. Additional data related to driver behavior are necessary to model lane changes with different reasons and driver characteristics.

MODEL DEVELOPMENT OVERVIEW This section presents a research framework based on empirical data for modeling lane changes on urban arterials (Figure 1). The first two steps relate to data collection, including a focus group study and an in-vehicle experiment. Focus group discussions help to identify driver attitudes toward lane-changing reasons and their corresponding behaviors. The results were analyzed to obtain insights for developing a plan for collecting field data. During the in-vehicle experiment effort, participants were recruited to drive an instrumented vehicle so that field values for specific important factors identified in the focus groups could be collected. Conclusions from this step were combined

Driver background

Step 1

Focus Group Study

Quantitative data Qualitative data

Step 2

InVehicle Driving Test

Driver Characteristics Classification I Driver Type 1

Driver Characteristics Classification II

Driver background

Quantitative data

Driver ... Driver Type 2 Type n

Driver Type 1

Classification I is consistent with Classification II ?

Driver ... Driver Type 2 Type m

N

Y

Step 3

Model for driver’s decision on each lane-changing reason

Gap acceptance models for various driver types and various types of lane-changing (free, cooperative/competitive, and forced)

Model for driver’s decision on each lane-changing reason

FIGURE 1

Proposed framework for modeling lane-changing behavior.

Sun and Elefteriadou

with focus group findings to test and validate the effectiveness of data collection. The third step involved model development. Data sets from the in-vehicle field experiments were used to construct submodels for driver decisions (based on focus group discussions) and the gap acceptance procedure. They eventually were used to determine whether a lane change was necessary and whether the gaps in the target lane were acceptable. The remainder of this section describes these steps in more detail. Focus Group Study Field-based surveys (revealed preference and stated preference) have been used to indicate driver preferences for various transportation scenarios (13). However, the survey results may not be accurate if the participants are not entirely truthful, are rushed, or do not fully comprehend each question. Focus group meetings encourage more critical thinking as a direct result of interacting with other participants and the moderator, thereby providing greater insight into why certain beliefs and opinions are held (14). In this research, focus groups were used to obtain driver perceptions and attitudes regarding lane-changing maneuvers on urban streets. Several urban lane-changing scenarios (e.g., stopped bus, vehicle merge, slow vehicle, queue advantage, heavy vehicle, tailgating, and pavement) were examined and discussed. The study objective was to obtain the reasons and factors that affect lane-changing execution so that driver characteristics could be incorporated into the lanechanging model. Detailed experimental design and implementation for the focus group study, along with the corresponding analysis of results, can be found elsewhere (15). Focus group participants were categorized according to their background information and verbal responses during the discussion so that characteristics could be identified for each driver type. Considering both personal background data and stated behavior data related to urban arterial lane-changing situations, drivers were classified into four groups: • Type A drivers would not change lanes in most situations. They are risk averse and always want to stay in the current lane. • Type B drivers would like to get a better position or speed advantage under some low-risk situations but not in others. They are more willing to change lanes to obtain speed advantage than Type A drivers and consequently are somewhat more aggressive. • Type C drivers like to get a better position or speed advantage if they have a chance. They are more ambitious than Type B drivers in getting a speed advantage and take more risks. • Type D drivers always try to get a better position or speed advantage whenever they have a chance; position and speed are their first considerations. They barely think about others, change lanes without hesitation, and take risks without caring about the environment or other drivers. Results of the focus group study include the four-type classification scheme and the factors affecting driver decisions under each lane-changing scenario that can be loaded in microsimulators to better replicate driver behavior in urban street networks. In-Vehicle Data Collection One issue with using focus groups is that the research is based on group discussions and background surveys in which participants may

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overthink their actions. Consequently, an in-vehicle experiment was designed to observe and validate stated driver preferences, observing driver actions under various lane-changing scenarios and obtaining quantitative values for the important factors identified during the focus group study. For the in-vehicle experiment, 10 drivers were recruited from the focus group participants to reflect a diversity of age, gender, driving experience, occupation, vehicle ownership, and perceived aggressiveness characteristics, then 30 more drivers were recruited to create a pool of 40 total participants. The road segments selected for the driving test were located in Gainesville, Fla. Each would potentially bring about one or more lane-changing scenarios, such as left turn, right turn, work zone, stopped bus, or right lane merging. During the in-vehicle driving test, each driver was accompanied by a researcher. As they proceeded through the developed route, the researcher recorded three driver actions: • Potential lane change—a lane change could have been attempted but was not; • Attempted (but unsuccessful) lane change—a lane change was attempted, but the maneuver was not completed; and • Completed lane change—the lane-changing maneuver was completed successfully. For each potential, attempted, and completed maneuver during the test, the time and location were recorded for further analysis. Scenarios were verified by communicating with the subject. Occasionally, the driver was asked to clarify interactions with other vehicles during the driving test. Information obtained from the in-vehicle experiments include participants’ personal background information, in-vehicle video clips for each driving test, and the researcher’s notes for lane-changing related maneuvers during each driving test. A total of 205 potential, 199 attempted, and 601 completed lane-changing maneuvers were recorded. Various analyses were conducted to summarize the information related to the research objectives. First, each maneuver was identified from the video clips. Values were quantified for the factors that the focus group had identified as important in affecting lane-changing scenarios. Driver performance and the vehicle movements related to surrounding vehicles (e.g., speed, reaction time, lane-changing gap acceptance, speed– distance relationship, speed perception, and vehicle-to-vehicle communication link) were measured. By the end of the data reduction, each lane-changing scenario was associated with a list of important factors (from the focus group study) and multiple sets of corresponding field values (collected from the in-vehicle experiment). Three types of data sets for lane-changing behavior were developed: potential, attempted-but-unsuccessful, and completed. Next, with the three types of data sets obtained for each driver, cluster analyses (similar to those conducted for the focus group data) were performed to categorize participating drivers according to their behavior. Two classification schemes were designed: one based on the driver’s background information (acquired from the check-in procedure) and one based on the driver’s lane-changing aggressiveness indices, measured from behaviors recorded during the in-vehicle driving test (16). The comparison and analysis further confirmed that in-vehicle participants could be categorized in four groups, as recommended in the focus group study. As a result, the in-vehicle data were classified and used to model lane-changing behaviors with different driver characteristics. The in-vehicle experiment was helpful in validating and confirming the conclusions from the focus group study and collecting the

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corresponding parameters. One potential bias of this type of data collection is that drivers who know they are being observed may modify their behavior. Calibration and validation were conducted during implementation to address this issue, as described in the next section.

Lane-Changing Model Development During model development, driver characteristics and field data obtained from the in-vehicle experiments were used to model the probabilistic decision under various discretionary lane changes (DLCs) and the gap acceptance procedure. A hierarchical framework for modeling strategies in plan choice (i.e., decision to change lanes) and action choice (i.e., gap acceptance) that incorporates driver characteristics is presented in Figure 2. First, to model the lane-changing probability under each scenario, the completed lane changes and attempted maneuvers are recognized as “accept” responses and potential maneuvers as “not accept” responses. Next, during gap acceptance modeling, only attempted and completed maneuvers are considered. Completed lane changes are labeled “acceptable” and attempted maneuvers “unacceptable.” In this way, the two processes form a special nested logit model (17). The development of the lane-changing probability model and the gap acceptance modeling procedure are described in the following subsections.

Scenario-Based Model of Lane-Changing Probability To model the decision to change lanes under each DLC scenario, the lane-changing maneuvers observed in the field were grouped by lane-changing scenario. Each scenario represents the particular DLC (e.g., changing lanes to pass a bus stopped at bus stop). With the provided outcome (accept or not accept) for each lane-changing behavior, the probability of changing lanes under each DLC scenario was estimated as a function of the associated important factors and driver types. The dependent variable is the outcome of a binary choice

Driver’s decision to change lanes

(1 = accept, 0 = not accept). A binary logistic regression was chosen to estimate the probability function of changing lanes under each scenario (18), calculated as P ( LC ) =

eV ( LC) 1 + eV ( LC)

(1)

where P(LC) is the probability of changing lanes under the given scenario and V(LC) is the utility of changing lanes under a given scenario, which is formulated as β0 + βT × X (where β0 is the constant, βT are the corresponding coefficients, and X is the independent variable vector). To assess the impact of important factors on each corresponding DLC and formulate a utility function, an example for the stopped bus scenario is provided; the other scenarios are analyzed elsewhere (15). The important factors identified for the stopped bus scenario from the focus group study are Factor 1 = traffic congestion in the target lane (Cgst), Factor 2 = queue ahead (Que), Factor 3 = location of the next downstream stop (LocStop) (in miles), Factor 4 = distance to the bus (Dist) (in feet), and Factor 5 = number of people at the bus stop (NP). With the field values obtained, along with the binary choice of outcomes, the utility function of changing lanes for this scenario is estimated as V ( LC ) = 6.48 − 0.236 × Cgst − 19.116 × LocStop − 0.381 × Dist − 2.533 × DrvTypeA − 1.303 × DrvTypeB − 1.139 × DrvTypeC

(2)

The factors Que and NP were excluded because they were not significant at the 90% confidence level. Three dummy variables were created to represent the three driver types (A, B, and C) for the

All LC related maneuvers

Completed and attempted LCs (accept)

Completed LCs (gap is acceptable)

Potential LCs (do not accept)

Attempted LCs (gap is unacceptable)

Plan

Driver characteristics

Action

Gap acceptance FIGURE 2

Modeling framework for plan and action choices in lane-changing behavior (LC ⴝ lane change).

Sun and Elefteriadou

regression analysis: DrvTypeA, DrvTypeB, and DrvTypeC. If a subject driver belongs to Type A, then DrvTypeA is equal to 1 and both DrvTypeB and DrvTypeC are equal to 0. For a Type D driver, all three dummy variables are equal to 0. By the end of development for the model of lane-changing probability, each preselected DLC scenario is related to a utility function of the respective factors and driver types and the probability of changing lanes can be calculated by Equation 1.

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The Newberry Road network was simulated and calibrated using the CORSIM original lane-changing algorithm (for 15 runs). For calibration, CORSIM’s parameter settings related to driver lane-changing behavior were adjusted such that • The CORSIM-simulated travel times were within ±10% of field-measured travel times for westbound (WB) and eastbound (EB) approaches and • The total numbers of lane-changing maneuvers were within ±20% of field-measured values for WB and EB approaches.

Gap Acceptance Model for Urban Arterials In addition to the scenario-based probability model, a new algorithm was proposed to model the lane-changing gap acceptance in three modes: free, forced, and cooperative and/or competitive (C/C). Free and forced lane changes are modeled as instantaneous events during the time interval immediately after the driver’s decision. In contrast, C/C lane changes are more complex and involve a sequence of vehicle interactions lasting for several seconds, and the merging vehicle may give up a lane-changing attempt in the end. The emphasis of this gap acceptance procedure was to model lane changes with interactions as a sequence of “hand-shaking negotiations” between vehicles. Various interaction scenarios based on driver actions and responses were considered by referring to the negotiation procedure used in computer network communications. Model formulation and implementation details can be found elsewhere (19).

MODEL IMPLEMENTATION AND VALIDATION The proposed lane-changing model was implemented and validated in the CORSIM microscopic traffic simulator. To distinguish it from the lane-changing model that is embedded in CORSIM (referred to as the “original” model), the newly developed lane-changing model is referred to as the “new” model. First, validation field data were collected and analyzed. The proposed model was implemented as a run-time extension (RTE) plug-in in CORSIM, followed by an aggregate calibration to tune up the selected behavioral parameters within the simulation model. Next, both calibrated lane-changing models (original and new) were simulated with the additional origin–destination (O-D) demands, and results were compared with the field measurements on the selected indices of measures. Various statistical analyses were conducted to evaluate agreement between results from the simulations and results from field observations.

Implementation and Calibration The traffic volume data (mainline, cross street, and heavy vehicle percentage) used to simulate the network in CORSIM were obtained from a Florida Department of Transportation publication citing data from Newberry Road, Gainesville, Fla. (20). Signal timing data for each intersection were obtained from the City of Gainesville, which were further confirmed from the video observation. Travel times for both directions between the I-75 northbound ramps and NW 66th Street were obtained through vehicle matching (manual observation of vehicles in video) and were used to calibrate the model.

In the next step, the new model was implemented as a C++ plug-in (.dll) that interfaces with the CORSIM engine during the simulation. Commands in a TShell environment were used for RTE deployment. In CORSIM v6.1, for each simulation time step, the CORSIM server calls a series of functions within CORSIM to drive the simulation event loop (Figure 3) (21). The RT_PRE_NETSIM_VEHICLE message is sent just before calling the FORTRAN subroutine MOVE (which handles lane changing, car following, and so on) to move all the vehicles for the current time step. The lane-changing plug-in was set up to respond to that message, and the function within the plug-in would perform the lane-changing maneuver. When the lane change was complete, the CORSIM lane-changing timer was set to a value that would prevent the embedded lane-changing logic from being applied. The subroutine MOVE would still be called, but vehicles would not be allowed to change lanes. Practically, CORSIM sets the maximum number of lanes for any link as 7, and a global index for any given lane (K) on road link IL is calculated as (IL − 1) × 7 + K. All vehicles in lane K are stored in a double-linked list data structure, which can be exported and accessed by the RTE plug-in. In modeling a lane change, the subject vehicle must be removed from the original lane link list and inserted into the corresponding position on the target lane link list. Both operations are completed in the same simulation time step, (t + 1). After the new connections between vehicles are set, all vehicle accelerations are calculated by the car-following model in CORSIM with respect to the lead vehicle in the same lane. Another implementation step is to assign CORSIM’s 10 driver types to the 4 proposed driver types (A, B, C, and D). The percentages from the in-vehicle driving test participants for each driver type (A, B, C, and D) were calculated as 9/40 = 22.5%, 12/40 = 30%, 11/40 = 27.5%, and 8/40 = 20%, respectively. To allocate the driver types according to these percentages, all of the CORSIM Type 1 and Type 2 drivers and 25% of Type 3 drivers were assigned to Type A; 75% of CORSIM Type 3 drivers, all Type 4 and Type 5 drivers, and 25% of Type 6 drivers were assigned to Type B; 75% of CORSIM Type 6 drivers and all Type 7 and Type 8 drivers were assigned to Type C; and all CORSIM Type 9 and Type 10 drivers were assigned to Type D. In the new model calibration, the estimated coefficients and parameters are included as initial settings in the CORSIM plug-in. The group of parameters that generates travel times and number of lane changes closest to field-measured values was identified for the simulation and validation. By the end of the adjustment, the average simulated travel times obtained for the new model were 56.1 s and 69.8 s for WB and EB traffic, respectively, which are within ±5% of the field-measured travel time. The numbers of lane changes (37 for WB and 50 for EB) were within ±20% for both approaches.

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Component Object Model (COM)

Run Time Extension (RTE) Step 1. RT_PRE_NETSIM_VEHICLE message Step 2. Message from RTE to indicate start CORWin Interface

TShell

CORSIM Driver Component

CORSIM Server (MOVE)

*Using API instead of shared memory

Exported Functions Exported Memory

CORSIM Property Pages

TSD/TID Interface

Files

CORSIM LaneChange .dll Plug-in (LC model)

Step 3. Extract/control information in shared memory 1. conduct lane-changing model 2. set lane-changing timer for veh.

FIGURE 3 CORSIM: entire architecture and communication with lane-changing plug-in [CORWin ⴝ interface that enables RTE to send messages to CORSIM server and to send text messages to be displayed by CORSIM driver; API ⴝ application programming interface; TSD ⴝ time-step data (generates time-step data files used by TRAFVU to animate simulated vehicles and signals); TID ⴝ time interval data [generates time interval data files used by TRAFVU to display measures of effectiveness data]. [Source: adapted from the CORSIM Run-Time Extension (RTE) Developer’s Guide (21).]

Validation The purpose of system validation is to test and determine the extent to which the simulation model replicates the real system under different traffic conditions (22). This section compares the simulated results of the two (original and new) models with the field data. Both calibrated models were simulated with O-D demands measured from video data under congested traffic conditions on different days. Three measures of performance were selected to evaluate the model performance: average lane-based travel time, vehicle lane distribution, and cumulative lane changes by vehicles (2, 19).

Lane-Based Travel Time The segment of Newberry Road used to simulate the network in CORSIM is divided into three sections by four signalized intersections. Average lane-based travel times were obtained from the video data by manually matching vehicles at the entrance and exit of each arterial section. For both directions, traffic flows on approaches were used to obtain the average travel time. Figure 4 compares average lane-based travel times for the new model, the validation field data, and the original model for each section. Results indicate that the original model tends to underestimate travel times; travel times in the new model are closer to the field data, especially for the right and left lanes. Additionally, after applying the new model, differences in by-lane travel time are more significant and closer to the field data than the original CORSIM simulation. Although the average travel times are similar, the new model better represents the lane-by-lane differences.

Travel times from the two simulations were compared with the validation field data. Chi-square tests were performed between the expected travel time (from field data) and the two observed travel times (from simulations) for westbound (WB) and eastbound (EB) traffic. For WB traffic, simulated travel times for the new and original cases were not statistically different from field travel times; however, the confidence level was greater when using the new algorithm (97%) than when using the original algorithm (82%). Similarly, for EB traffic, simulated travel times for the new and original cases were not statistically different from the field travel times; the confidence level was greater with the new algorithm (95%) than with the original one (65%). Two-sided T-tests were performed to investigate whether lanebased travel times from the new and original models were equal to those of the field data. With the new model, the travel time was not different for all travel times (95% confidence level) except for the EB right-lane travel time in Section 3. With the original model, the EB right-lane travel time for Section 3 and the WB left-lane travel time for all three sections were significantly different from field-measured values (95% confidence level). In addition, the two algorithms were compared (Table 1). An F-test was conducted to compare variances, and the variances were the same in all but one case (4.372 > F (95%) = 2.480 for Section 3, WB, Lane 1). Because the sample size was relatively small (15 runs), a T-test with equal and unequal (and unknown) variances was selected to compare the means at a 95% confidence level. The comparison results indicate that with the new model, the changes in travel times in the left and right lanes are different for all three sections (except in Section 3, WB, Lane 1 and in Section 3, EB, Lane 3). However, the travel times for the middle lane (except in Section 2, EB, Lane 2) do not differ much between the two algo-

Sun and Elefteriadou

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Field Data 8

CORSIM

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(e)

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(f)

FIGURE 4 Comparison of lane-based average travel times for Section 1: (a) WB and (b) EB; for Section 2: (c) WB and (d ) EB; and for Section 3: (e) WB and (f ) EB.

rithms. One potential explanation is that the most defensive drivers or those unwilling to change lanes would stay in the middle lane, which implies that the new model did not affect vehicles in the middle lane as much as those in the left and right lanes.

Lane Distribution Vehicle lane distributions were obtained from video data and compared with the results of the simulations (20). In both CORSIM simulations, surveillance detectors were placed on each lane for

every 50 ft to record the number of vehicles using the lane. Field lane use data were observed and aggregated to obtain the percentages of the traffic distributions on each lane. Figure 5 compares lane distributions from the new model, validation field data, and the original model. Figure 5a presents the simulation results for WB traffic in the new and original models, which are similar: Both tend to overestimate use of the middle lane (Lane 2) and underestimate use of the left lane (Lane 3). However, results from the new model are closer to fieldobserved data for Lanes 2 and 3. The root-mean-square error (RMSE) was calculated for the vehicle lane distributions in the original and new

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TABLE 1

Comparison of Simulation Travel Time Between New and Original CORSIM Models Mean TT (sec)

Std. Dev. (sec)

New Model

Original Model

New Model

Original Model

Pooled

F-Test

T-Test

Is TT Statistically Different?

Section 1 WB Lane 1 Lane 2 Lane 3

15.3 11.7 17.5

12.7 12.3 14.1

2.66 2.71 3.12

2.93 2.63 2.05

2.798 2.670 2.640

1.213 1.062 2.316

2.545 0.615 3.527

Yes No Yes

Section 1 EB Lane 1 Lane 2 Lane 3

24.4 18.4 24.3

21.8 19.5 19.2

3.13 2.67 3.32

3.72 3.26 2.49

3.438 2.980 2.934

1.413 1.491 1.778

2.071 1.011 4.760

Yes No Yes

Section 2 WB Lane 1 Lane 2 Lane 3

44.8 39.7 45.9

39 39.4 37.1

5.07 5.34 7.23

5.96 5.62 6.94

5.533 5.482 7.086

1.382 1.108 1.085

2.871 0.150 3.401

Yes No Yes

Section 2 EB Lane 1 Lane 2 Lane 3

30 20 24

25 16.6 28

4.77 4.23 3.61

4.06 5.01 4.49

4.429 4.636 4.074

1.380 1.403 1.547

3.092 2.008 2.689

Yes Yes Yes

Section 3 WB Lane 1 Lane 2 Lane 3

23.4 19.9 17.5

21.6 17.3 18.8

4.83 4.32 2.06

2.31 5.34 1.49

3.786 4.857 1.798

4.372 1.528 1.911

1.302 1.466 1.980

No No Yes

Section 3 EB Lane 1 Lane 2 Lane 3

10.5 13.9 8.7

12.5 13.4 8.4

1.83 2.32 1.56

2.31 2.34 1.79

2.084 2.330 1.679

1.593 1.017 1.317

2.628 0.588 0.489

Yes No No

Location

NOTE: TT = travel time.

CORSIM models as 0.051 and 0.0374, respectively, which indicates an improvement of 26.62%:

RMSE =

2 1 3 ∑ (Y isim − Y obsi ) 3 i =1

(3)

where i is the lane index (1, 2, and 3 are right, middle, and left lanes, respectively) and Y isim and Y iobs are the simulated and observed percentage of vehicle lane distributions on lane i, respectively. Figure 5b presents results for EB traffic in the new and original models. Like the WB traffic results, both simulations overestimate use of the middle lane (Lane 2) and underestimate use of the left lane (Lane 3). The only difference is that the new model tends to overestimate use of the right lane (Lane 1), whereas the original model tends to underestimate this value. Overall, results from the new model are closer to field observations for all three lanes. The RMSE was calculated for the vehicle lane distributions in the original and new CORSIM models as 0.0571 and 0.0294, respectively, which indicates an improvement of 48.49%.

Vehicle-Based Cumulative Number of Lane Changes The cumulative number of lane changes by vehicles was observed from the video and compared with results from the new and original model simulations. The CORSIM original model underpredicts changes of more than one lane, probably because the CORSIM model considers only destination, incident (including work zone),

and lane use restrictions as reasons for changing lanes; other potential prevailing road scenarios are not taken into account. By incorporating the lane-changing probability model, the new model performs much better than the original one, particularly in terms of predicting the higher number of lane changes. The RMSE was calculated for the percentage of vehicles in the original and new CORSIM models as 0.0397 and 0.0275, respectively, which indicates an improvement of 30.71%:

RMSE =

2 1 4 ∑ (Y isim − Y obsi ) 4 i =1

(4)

where i is the number of lane changes (1, 2, 3, or 4) by vehicles and Y isim and Y iobs are the percentages of vehicles simulated and observed, respectively, with the number of lane changes equal to i.

CONCLUSIONS AND RECOMMENDATIONS The lane-changing model presented in this paper integrates components for lane-changing probability and gap acceptance. The emphasis is on modeling lane-changing maneuvers by using data related to driver behavior along with driver background and characteristics. A focus group study and in-vehicle driving tests were conducted to obtain data related to driver behavior and corresponding driver characteristics, and the probability of changing lanes under various DLC scenarios was estimated as a function of the associated important factors and driver types.

Sun and Elefteriadou

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40 35

Vehicles in lane (%)

30 25 New Field Data

20

CORSIM 15 10 5 0 Lane 1 (Right)

Lane 2 (Middle)

Lane 3 (Left)

(a) 45 40

Vehicles in lane (%)

35 30 New

25

Field Data 20

CORSIM

15 10 5 0 Lane 1 (Right)

FIGURE 5

Lane 2 (Middle) (b)

Lane 3 (Left)

Comparison of lane distributions for entire arterial (a) WB and (b) EB.

With the use of gap acceptance strategies observed in the field and behavior-related values collected during in-vehicle driving tests, a new algorithm for lane-changing gap acceptance was developed to model lane changes on urban arterials in three modes: free, forced, and C/C. The model was implemented in CORSIM, and the resulting average lane travel times, lane distributions, and cumulative numbers of lane changes were compared with field-observed data. Validation results indicate that by adopting the new algorithm, the simulator can model the interlane travel time differences more realistically than the original CORSIM algorithm can. Lane use and cumulative number of lane changes also were modeled more accurately after implementing the new lane-changing model. Although the results are promising, additional studies must be conducted to improve model performance. First, the model based on empirical data in this research was developed specifically for

lane changes on urban arterials. As a follow-up study, the modeling framework could be extended to other transportation facilities or other driver behaviors (such as car following) and the impact of driver characteristics on the evaluated behavior(s). In these cases, different data related to driver behavior would need to be collected to model the maneuvers by various categories. Second, data collection is a significant component in driver behavior modeling and especially important in lane change research. In this study, video methods and instrumented vehicles (with in-vehicle navigation devices) were used to collect field data on typical urban arterial segments. Additional studies may consider technologies for collecting data from more versatile geometric characteristics. To this end, virtual reality driving simulators may be used to collect data in situations that are otherwise difficult to observe and to control driving factors that affect behavior.

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Finally, the CORSIM microscopic simulator was selected as the test platform in this study. Other widely used commercial simulators (e.g., Aimsun, PARAMICS, and VISSIM) also could be considered as test beds for model implementation and validation, forming an important extension to the current research.

ACKNOWLEDGMENTS The authors appreciate support from the Young Excellent Faculty Plan in TongJi University, the National High Technology Research and Development Program of China, and the National Natural Science Foundation of China. The thorough comments from the anonymous referees also are greatly appreciated.

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