Charisma F. Choudhury

Lane-changing model with explicit target lane choice

The lane-changing model is an important component of microscopic traffic simulation tools. With the increasing popularity of these tools, a number of lane-changing models have been proposed and implemented in various simulators in recent years. Most of these models are based on the assumption that drivers evaluate the current and adjacent lanes and choose a direction of change (or no change) on the basis of the utilities of these lanes only. The lane choice set is therefore dictated by the current position of the vehicle and in multilane facilities would be restricted to a subset of the available lanes. Thus, existing models lack an explicit tactical choice of a target lane and therefore cannot explain a sequence of lane changes from the current lane to this lane. In this paper, a generalized lane-changing model that explicitly incorporates the choice of target lane is presented. The target lane is the lane that the driver perceives to be the best when a wide range of factors and goals are taken into account. The immediate direction in which a driver changes lanes is determined by the target lane choice. All parameters of the model were jointly estimated with detailed vehicle trajectory data. The model was validated and compared with an existing lane-changing model with the use of a microscopic traffic simulator. The results indicate that the proposed model performs significantly better than the previous model.

Traffic Simulation with MITSIMLab

MITSIMLab (MIcroscopic Traffic SIMulation Laboratory) is a microscopic traffic simulation model that evaluates the impacts of alternative traffic management system designs at the operational level and assists in their subsequent refinement. MITSIMLab models the travel and driving behavior of individual vehicles, the detailed movement of transit vehicles, and the various control and information provision strategies through a generic controller. A calibration methodology for important parameters and inputs was also developed. The model has been extended to address the special driving behavior evidenced in urban networks and has been used as a test bed for the evaluation of advanced traveler information systems (ATIS). Calibration and validation results from networks in the United States and Europe are discussed.

Modeling Acceleration Decisions for Freeway Merges

In uncongested traffic situations, a merge is executed when the available gap is sufficiently large. However, in congested traffic, acceptable gaps for merging are often not readily available, and merging can involve more complex mechanisms. For example, the driver in the target lane may slow down and cooperate with the merging driver, or the merging driver may become impatient and decide to force in, and compel the lag driver in the target lane to slow down. Choices of the merging plan or tactic affect the gap acceptance and acceleration decisions of the driver. A driver who has decided to force in, for instance, is likely to accept smaller gaps and accelerate to facilitate the merge. The chosen tactic at any instant, however, is not distinctly observable from the vehicle trajectory. The model presented in this paper extends previous research in modeling the effect of merging plans in the lane-changing decisions by integrating the acceleration decisions of the driver with the gap acceptance decisions. A combined model for merging plan choice, gap acceptance, target gap selection, and acceleration decisions of drivers merging from the on-ramp is developed in that regard. Parameters of all components of the models are estimated jointly with detailed vehicle trajectory data collected from Interstate 80 in California. The inclusion of the target gap choice and acceleration behavior components has been supported by a validation case study in which the model has been implemented in MITSIMLab and validated against the observed aggregate traffic data collected from US-101 in California.

Lane Selection Model for Urban Intersections

A lane choice model for urban arterial intersections is presented. This model replaces the rule-based heuristics to assign vehicles in their subsequent lanes used in state-of-the-art microscopic traffic simulators. The lane choice at intersections is modeled as a two-step process: target lane choice and immediate lane selection based on the selected target lane. The target lane is one that the driver perceives as the best to be in, considering a wide range of factors and goals. These include path plan considerations and lane-specific attributes and can vary with the planning capability and aggressiveness of the driver. However, a maneuver to the target lane may not be possible immediately. The observed trajectories consist only of the immediate lane choices of the drivers. The choice of immediate lane is conditional on the target lane selection and affected by maneuverability considerations and aggressiveness of the driver. The parameters of the target lane and immediate lane choice models are jointly estimated with detailed vehicle trajectories. The heterogeneities of the driver population, for both planning capability and aggressiveness, are explicitly taken into account in the model formulation. The estimated model is compared with a single-level intersection lane choice model to demonstrate the improvements in the goodness of fit. The improvements are further strengthened by validation studies within the microscopic traffic simulator MITSIMLab, where the simulation results using the proposed model yield better matches with observed data compared to the rule-based models.

Modelling driving decisions: a latent plan approach

Microscopic traffic simulation tools are becoming increasingly popular in evaluating transport options. Driving behaviour models (e.g. route choice models, lane-changing models, etc.) are essential components of these tools. The state-of-the-art driving behaviour models assume that drivers make instantaneous decisions. However, in reality, many of the driving decisions are based on a specific plan. The plan is however unobserved or latent and only the manifestations of the plan through actions are observed. Examples include selection of a target lane before execution of the lane change, choice of a merging tactic before execution of the merge. Ignoring the effect of plans in the decision framework can lead to incorrect representation of congestion in traffic simulation tools. In this article, we present a modelling methodology to address the effects of unobserved plans in the decisions of the drivers. The actions of the driver are conditional on the current plan and can be influenced by anticipation of downstream traffic conditions. The heterogeneity in decision making and planning capabilities of drivers are explicitly addressed. The methodology has been applied in developing lane-changing behaviour models with disaggregate trajectory data extracted from video recordings of an urban road using the maximum likelihood technique. Estimation results show that the latent plan models have a significantly better goodness-of-fit compared to the ‘reduced form’ models where the latent plans are ignored. The latent plan models were also found to outperform the reduced form models in validation case studies within the microscopic traffic simulator MITSIMLab.

Modeling of Travel Time Variations on Urban Links in London

An econometric framework was developed to combine data from various sources to identify the key factors contributing to travel time variations in Central London. Nonlinear latent variable regression models that explicitly accounted for measurement errors in the data were developed to combine data extracted from automatic number plate recognition cameras and automatic traffic counters. This procedure significantly differed from previous research in this area that was based primarily on traffic flow data and ignored measurement errors. The results indicate that nonlinear latent variable regression models can effectively explain travel time variations on a regular day by using variables related to vehicle type, traffic density, and traffic composition. Test results indicate that the proposed framework for correcting measurement errors yields significant improvements over base models, where such errors are ignored. The findings from the study validate some key hypotheses regarding influences of various factors on speed of urban traffic streams and can serve as a tool for investigation of the causes of traffic congestion. The model framework is general enough for application in other cases in which traffic data have similar measurement errors.

Transferability of Car-Following Models Between Driving Simulator and Field Traffic

During the past few decades, there have been two parallel streams of driving behavior research: models using trajectory data collected from the field (using video recordings, GPS, etc.) and models using data from driving simulators (in which the behavior of the drivers is recorded in controlled laboratory conditions). Although the former source of data is more realistic, it lacks information about the driver and is typically not suitable for testing effects of future vehicle technologies and traffic scenarios. In contrast, driving behavior models developed with driving simulator data may lack behavioral realism. However, no previous study has compared these two streams of mathematical models and investigated the transferability of the models developed with driving simulator data to real field conditions in a rigorous manner. The current study aimed to fill this research gap by investigating the transferability of two car-following models between a driving simulator and two comparable real-life traffic motorway scenarios, one from the United Kingdom and the other one from the United States. In this regard, stimulus–response–based car-following models were developed with three microscopic data sources: (a) experimental data collected from the University of Leeds driving simulator, (b) detailed trajectory data collected from UK Motorway 1, and (c) detailed trajectory data collected from Interstate 80 in California. The parameters of these car-following models were estimated by using the maximum likelihood estimation technique, and the transferability of the models was investigated by using statistical tests of parameter equivalence and transferability test statistics. Estimation results indicate transferability at the model level but not fully at the parameter level for both pairs of scenarios.

Bap re Bap!: Driving Experiences through Multimodal Unruly Traffic on Bumpy Roads

Congestion, lack of compliance to traffic laws, multimodal traffic, opportunistic decision making and poor road conditions are few of the key challenges faced by drivers in a developing countrys metropolitan city such as, Dhaka, Bangladesh. The drivers experience is affected by such road conditions which in turn shapes up their driving behavior and thus affects the traffic conditions which has been studied using sensor enabled tools as well as qualitative methods from a developing countrys context.

A comprehensive model to capture the preference for mass rapid transit in Dhaka: The State of the Art and the State of Practice

Dhaka, the capital of Bangladesh and one of the fastest growing megacities of the world, is already subjected to acute traffic congestion on a regular basis. Increasing the physical capacity to relieve congestion is however not feasible since already more than 70% of the area is built-up (Bari and Hasan, 2001). This has recently prompted the Government to prioritize the introduction of Mass Rapid Transit (MRT) options like Bus Rapid Transit (BRT) and Metro Rail in the city. Planning these MRT options however require rigorous mode choice models that can be used to predict ridership and quantify the willingness-to-pay (WTP) of the travellers. Though Dhaka is an old city (dating back to 16 th century), very few travel demand models have been developed for the city so far. Among the previous studies, four step travel demand models were developed in Dhaka Metropolitan Area Integrated Transport Study (DITS, 1993), Strategic Transport Plan (STP, 2005) and Dhaka Urban Transport Network Development Study (DHUTS, 2010) as well as by Habib (2002) and Hasan (2007) . However, in each case, the mode choice component was simplified and had grave limitations. In DITS, the mode choice model was simplified to a binomial choice model between private and public modes. In Habib (2002), an MNL model structure was adapted for the mode choice but the calibration results were counterintuitive with positive sign of the coefficients for time and cost parameters. In STP (2005), which is the most extensive travel demand model for Dhaka in recent years, a wide-scale household interview survey has been conducted for the first time. In the mode choice component of STP (2005), only two modes were considered i.e. Public Transport (PT) and Individualized Motorized Vehicles (IMV). In the IMV group, cars and taxis were grouped together overlooking their very different attributes (e.g. running cost, availability, accessibility, etc.) and the non-motorized vehicles (rickshaw) were not considered in spite of the fact that 37% of the person trips in Dhaka were made by rickshaw as reported in the same study (STP, 2005). Further, the STP model has adapted pre-set rules for determining choice-sets and ignored the heterogeneity among respondents. In Hasan (2007), a rule based choice model was adapted for car and a Multinomial Logit (MNL) model was adapted for the choice among rickshaw, auto-rickshaw, taxi and bus. Hasan’s model was based on STP data but the level-of-service (LOS) variables were updated using supplemental survey (for cost) and outputs of the software EMME/2 (for travel time). The potential measurement errors introduced in this process have however been ignored. In DHUTS (2010), a two step mode choice model has been developed where only two explanatory variables have been used: travel cost and Origin-Destination (O-D) shortest path distance (derived from network analysis). As evident from the description above, the existing mode choice models for Dhaka are based on pre set rules, ad-hoc choice-sets and network derived LOS values (without any correction for measurement errors). Further, the models are not robust enough to account for the new MRT, particularly since the LOS of MRT will vary significantly from the current modes. It may be noted that, the limitations of the available datasets played key roles behind the deficiencies of the previously developed mode choice models and this has prompted the current research where we present a comprehensive mode choice model which overcomes the limitations of the previous models and is robust enough to capture the preferences for the proposed MRT modes. In this paper, the STP data have been explored in detail, the key modeling issues have been identified and modeling approaches have been proposed to account for the data limitations. The improvements from the proposed approaches have been demonstrated by comparing the Value of Time (VOT) values. The rest of the paper is organized as follows: A short description of the Revealed Preference (RP) data highlighting the main limitations of the data and the description of the Stated Preference (SP) data collected as part of this research are presented first. This is followed by a description of the model framework. In the subsequent section, the estimation results of all the model components are presented which is followed by the VOT comparisons. The summary of findings and directions of future research are presented in the end.

Modeling Preference for School Bus Service in Dhaka, Bangladesh: Stated Preference Approach

This paper analyzes the factors that affect the choice of school buses in Dhaka, Bangladesh, the 11th largest city in the world. A multinomial logit mode choice model for school trips was developed with stated preference (SP) surveys of parents of students from a premier school zone. The design of the SP survey was challenging in several aspects, including the lack of background data on school traffic, the wide range of motorized and nonmotorized modes available for school trips, and the substantial heterogeneity in the levels of service of the various modes. A focus group was therefore conducted first, and the preliminary information was used to design the final SP survey, a paper survey based on fractional factorial design. The results show that for school buses, there are strong cost and time sensitivities as well as a significant preference for increased comfort levels. Significant market segmentation also exists for households that have high incomes, mothers who do not work outside the home, or both. The results are expected to help the Dhaka Transport Coordination Board formulate policies pertaining to school buses. The findings can also be useful for other developing countries, especially those in Asia, which share similar socioeconomic patterns.