Estimation of an integrated driving behavior model

This paper presents the methodology and results of estimation of an integrated driving behavior model that attempts to integrate various driving decisions. The model explains lane changing and acceleration decisions jointly and so, captures inter-dependencies between these behaviors and represents drivers’ planning capabilities. It introduces new models that capture drivers’ choice of a target gap that they intend to use in order to change lanes, and acceleration models that capture drivers’ behavior to facilitate the completion of a desired lane change using the target gap.The parameters of all components of the model are estimated simultaneously with the maximum likelihood method and using detailed vehicle trajectory data collected in a freeway section in Arlington, Virginia. The estimation results are presented and discussed in detail.     

Categorization of the lane change decision process on freeways

Traffic operations for new road layouts are often simulated using microscopic traffic simulation packages. These traffic simulation packages usually simulate traffic on freeways by a combination of a car-following model and a lane change model. The car-following models have gained attention of researchers and are well calibrated versus data. The proposed lane change models are often representations of assumed reasonable behavior, not necessarily corresponding to reality. The current simulation packages apply solely one specific type of model for car-following or lane changing for all vehicles during the simulation. This paper investigates the decision process of lane changing maneuvers for a variety of drivers based on a two-stage test-drive. Participants are asked to take a drive on a freeway in the Netherlands in a camera-equipped vehicle. Afterwards, the drivers are asked to comment on their choices related to lane and speed choice, while watching the video. This paper reveals that different drivers have completely different strategies to choose lanes, and the choices to change lane are related to their speed choice. Four distinct strategies are empirically found. These strategies differ not only in parameter values, as is currently being modeled in most simulation packages, but also in their reasoning. Most remarkably, all drivers perceive their strategy as an obvious behavior and expect all other drivers to drive in a similar way. In addition to the interviews of the participants in the test-drive, 11 people who did not take part in the experiment were interviewed and questioned on lane change decisions. Moreover, the findings of this study have been presented to various groups of audience with different backgrounds (about 150 people). Their comments and feedback on the derived driving strategies have added some value to this study. The findings in this paper form a starting point for developing a novel lane change model which considers four different driving strategies among the drivers on freeway. This is a significant contribution in the area of driving behavior modeling, since the existing microscopic simulators consider only one type of lane change models for all drivers during the simulation. This could lead to significant changes in the way lane changes on freeways are modeled.     

Optimal deployment of charging lanes for electric vehicles in transportation networks

Given the rapid development of charging-while-driving technology, we envision that charging lanes for electric vehicles can be deployed in regional or even urban road networks in the future and thus attempt to optimize their deployment in this paper. We first develop a new user equilibrium model to describe the equilibrium flow distribution across a road network where charging lanes are deployed. Drivers of electric vehicles, when traveling between their origins and destinations, are assumed to select routes and decide battery recharging plans to minimize their trip times while ensuring to complete their trips without running out of charge. The battery recharging plan will dictate which charging lane to use, how long to charge and at what speed to operate an electric vehicle. The speed will affect the amount of energy recharged as well as travel time. With the established user equilibrium conditions, we further formulate the deployment of charging lanes as a mathematical program with complementarity constraints. Both the network equilibrium and design models are solved by effective solution algorithms and demonstrated with numerical examples.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

Maneuvers of motorcycles in queues at signalized intersections

The maneuvering models of motorcycles in previous studies often considered motorcycles' traveling in terms of movements in a physical static lane and not in terms of dynamic virtual lane‐based movements. For that reason, these models are not able to imitate motorcyclists' behavior well. This paper proposes a maneuverability model framework for motorcycles in queues at signalized intersections with considering the dynamic motorcycle's lane. The model includes (i) a dynamic motorcycle's lane to identify the current, left, and right lanes of the subject motorcycle, (ii) a threshold distance to determine when a motorcyclist starts to consider maneuvering, (iii) a lane selection model to identify the lane preferred by a motorcyclist, and (iv) a gap acceptance model to describe whether or not the lead and lag gaps are acceptable for maneuvering. The model framework captures the variation across the motorcyclist population and over time observations. The models were applied to Hanoi and Hochiminh city, Vietnam, based on microscopic data collected from video images. All of the parameters were estimated using the maximum likelihood method with the statistical estimation software GAUSS. The results show that 77.88% of the observed maneuvers – either staying in the current lane or turning left or right – could be modeled correctly by the proposed models. Copyright © 2010 John Wiley & Sons, Ltd.     

Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework

The objective of this paper is to quantify and characterize driver behavior under different roadway geometries and weather conditions. In order to explore how a driver perceives the rapidly changing driving surrounding (i.e. different weather conditions and road geometry configurations) and executes acceleration maneuvers accordingly, this paper extends a Prospect Theory based acceleration modeling framework. A driving simulator is utilized to conduct 76 driving experiments. Foggy weather, icy and wet roadway surfaces, horizontal and vertical curves, and different lane and shoulder widths are simulated while having participants driving behind a yellow cab at speeds/headways of their choice. After studying the driving trends observed in the different driving experiments, the extended Prospect Theory based acceleration model is calibrated using the produced trajectory data. The extended Prospect Theory based model parameters are able to reflect a change in risk-perception and acceleration maneuvering when receiving different parameterized exogenous information. The results indicate that drivers invest more attention and effort to deal with the roadway challenges compared to the effort to deal with the weather conditions. Moreover, the calibrated model is used to simulate a highway segment and observe the produced fundamental diagram. The preliminary results suggest that the model is capable of capturing driver behavior under different roadway and weather conditions leading to changes in capacity and traffic disruptions.     

A two-dimensional simulation model for modelling turning vehicles at mixed-flow intersections

The turning behavior is one of the most challenging driving maneuvers under non-protected phase at mixed-flow intersections. Currently, one-dimensional simulation models focus on car-following and gap-acceptance behaviors in pre-defined lanes with few lane-changing behaviors, and they cannot model the lateral and longitudinal behaviors simultaneously, which has limitation in representing the realistic turning behavior. This paper proposes a three-layered “plan-decision-action” (PDA) framework to obtain acceleration and angular velocity in the turning process. The plan layer firstly calculates the two-dimensional optimal path and dynamically adjusts the trajectories according to interacting objects. The decision layer then uses the decision tree method to select a suitable behavior in three alternatives: car-following, turning and yielding. Finally, in the action layer, a set of corresponding operational models specify the decided behavior into control parameters. The proposed model is tested by reproducing 210 trajectories of left-turn vehicles at a two-phase mixed-flow intersection in Shanghai. As a result, the simulation reproduces the variation of trajectories, while the coverage rate of the trajectories is 88.8%. Meanwhile, both the travel time and post-encroachment time of simulation and empirical turning vehicles are similar and do not show statistically significant difference.     

Speed-spacing dependency on relative speed from the adjacent lane: New insights for car following models

This paper examines the traffic dynamics underlying a recently observed phenomenon, the so called “sympathy of speeds” whereby a high occupancy vehicle (HOV) lane seemingly exhibits lower vehicular capacity and lower flow at speeds throughout the congested regime compared to the adjacent general purpose (GP) lanes. Unlike previous studies this paper examines a time-of-day HOV lane. During the non-HOV periods the study lane reverts to a GP lane, thereby providing a control condition for the specific lane and location. This work uses the single vehicle passage (svp) method to group vehicle passages before measuring the traffic state and extends the svp to bin vehicles in the study lane based on the relative speed to the adjacent lane. The extended svp method allows the work to also study the impacts during the non-HOV periods when the study lane serves GP vehicles. This work finds that: (1) during the non-HOV periods the study lane exhibited behavior indistinguishable from the adjacent GP lane. (2) The sympathy of speeds persists throughout the day, even when the study lane serves GP vehicles. (3) The relative speed to the adjacent lane provided a better predictor of behavior than whether or not the HOV restriction is active. In short, the car following behavior that gives rise to the sympathy of speeds is unrelated to the HOV restriction per se, persisting under GP operations as well.This dependency on the relative speed in the adjacent lane is an important finding given the fact that most existing car following models assume that the longitudinal acceleration of a following vehicle is strictly a function of the relationship to the leading vehicle in the same lane. Because drivers in general adopt a larger spacing when faced with a high differential in speed between lanes means that car following behavior also depends on the relative speed to the adjacent lane. This fact has likely gone unnoticed to date because generally the conditions that give rise to a differential in speeds between lanes are usually short lived, and thus, do not become apparent in conventional macroscopic data except under exceptional circumstances that include confounding factors like HOV operations.     

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.     

A macroscopic traffic model for highway work zones: Formulations and numerical results

This study presents a multilane model for analyzing the dynamic traffic properties of a highway segment under a lane‐closure operation that often incurs complex interactions between mandatory lane‐changing vehicles and traffic at unblocked lanes. The proposed traffic flow formulations employ the hyperbolic model used in the non‐Newtonian fluid dynamics, and assume the lane‐changing intensity between neighboring lanes as a function of their difference in density. The results of extensive simulation experiments indicate that the proposed model is capable of realistically replicating the impacts of lane‐changing maneuvers from the blocked lanes on the overall traffic conditions, including the interrelations between the approaching flow density, the resulting congestion level, and the exiting flow rate from the lane‐closure zone. Our extensive experimental analyses also confirm that traffic conditions will deteriorate dramatically and evolve to the state of traffic jam if the density has exceeded its critical level that varies with the type of lane‐closure operations. This study also provides a convenient way for computing such a critical density under various lane‐closure conditions, and offers a theoretical basis for understanding the formation as well as dissipation of traffic jam.     

A problem of limited-access special lanes. Part I: Spatiotemporal studies of real freeway traffic

Most special-use freeway lanes in the US, whether reserved for carpools, toll-paying commuters or both, are physically separated from the adjacent regular-use lanes by some form of barrier. Vehicle movements in and out of a special lane of this type are permitted only at select access points along the route. The barrier at each select point might open for a distance of 400 m or so. Limiting access in this way is said to reduce the “turbulence” that might otherwise occur were the special lane not to have a barrier, such that vehicles could instead enter or exit that lane anywhere along its length.Yet, real freeway traffic studied in spatiotemporal fashion shows that access points are prone to become bottlenecks. The problem occurs when traffic in the regular lanes becomes dense, as commonly happens during a rush. Drivers then seek refuge in the special lane in greater numbers. Since the vehicular maneuvers through the access point are focused within a limited physical space, they can become disruptive and further degrade traffic. Degradation can occur both in the special lane and in the adjacent regular ones. The damage can be worse than when there is no barrier to limit special-lane ingress and egress.The problem is shown to be reproducible across sites and across days at each site. Policy implications are discussed. Select designs and policies to address the problem are thereafter explored in Part II of the paper using traffic simulation.     

Explaining high-occupancy-toll lane use

In recent years, high-occupancy-toll lanes have emerged as an increasingly popular alternative to high-occupancy-vehicle lanes for solving the problems of traffic congestion and air pollution. However, the existing literature on the use of high-occupancy-toll lanes has attended much to their impacts to the neglect of their determinants. An understanding of why people choose to use high-occupancy-toll lanes will shed light on policy decisions concerning high-occupancy-toll lane investments and developments. To fill this void, this study examines the determinants of high-occupancy-toll lane use with the first comprehensive survey data on the State Route 91 Express Lanes in California and multivariate logistic regression models. The results show that controlling for other variables, household income, vehicle occupancy, commute trip, and age are important predictors of high-occupancy-toll lane use, but gender, trip length, trip frequency, and other household characteristics make no significant differences in high-occupancy-toll lane use. Moreover, contrary to the conventional wisdom, work-to-home trips are found to be more likely to use high-occupancy-toll lanes than home-to-work and other trips. These findings provide some useful indications for the implementation of high-occupancy-toll lanes and future research.     

Deploying public charging stations for electric vehicles on urban road networks

This paper explores how to optimally locate public charging stations for electric vehicles on a road network, considering drivers’ spontaneous adjustments and interactions of travel and recharging decisions. The proposed approach captures the interdependency of different trips conducted by the same driver by examining the complete tour of the driver. Given the limited driving range and recharging needs of battery electric vehicles, drivers of electric vehicles are assumed to simultaneously determine tour paths and recharging plans to minimize their travel and recharging time while guaranteeing not running out of charge before completing their tours. Moreover, different initial states of charge of batteries and risk-taking attitudes of drivers toward the uncertainty of energy consumption are considered. The resulting multi-class network equilibrium flow pattern is described by a mathematical program, which is solved by an iterative procedure. Based on the proposed equilibrium framework, the charging station location problem is then formulated as a bi-level mathematical program and solved by a genetic-algorithm-based procedure. Numerical examples are presented to demonstrate the models and provide insights on public charging infrastructure deployment and behaviors of electric vehicles.     

Traffic evacuation simulation based on multi-level driving decision model

Traffic evacuation is a critical task in disaster management. Planning its evacuation in advance requires taking many factors into consideration such as the destination shelter locations and numbers, the number of vehicles to clear, the traffic congestions as well as traffic road configurations. A traffic evacuation simulation tool can provide the emergency managers with the flexibility of exploring various scenarios for identifying more accurate model to plan their evacuation. This paper presents a traffic evacuation simulation system based on integrated multi-level driving-decision models which generate agents’ behavior in a unified framework. In this framework, each agent undergoes a Strategic, Cognitive, Tactical and Operational (SCTO) decision process, in order to make a driving decision. An agent’s actions are determined by a combination, on each process level, of various existing behavior models widely used in different driving simulation models. A wide spectrum of variability in each agent’s decision and driving behaviors, such as in pre-evacuation activities, in choice of route, and in the following or overtaking the car ahead, are represented in the SCTO decision process models to simulate various scenarios. We present the formal model for the agent and the multi-level decision models. A prototype simulation system that reflects the multi-level driving-decision process modeling is developed and implemented. Our SCTO framework is validated by comparing with MATSim tool, and the experimental results of evacuation simulation models are compared with the existing evacuation plan for densely populated Beijing, China in terms of various performance metrics. Our simulation system shows promising results to support emergency managers in designing and evaluating more realistic traffic evacuation plans with multi-level agent’s decision models that reflect different levels of individual variability of handling stress situations. The flexible combination of existing behavior and decision models can help generating the best evacuation plan to manage each crisis with unique characteristics, rather than resorting to a fixed evacuation plan.     

Methods of dropping auxiliary lanes at freeway weaving segments

Auxiliary lanes connecting freeway entrance and exit ramps provide additional space for entering and exiting vehicles to change lanes. The method of dropping auxiliary lanes is critical in the design of freeway auxiliary lanes. This study investigates the performance of different methods of dropping auxiliary lanes. Case studies were conducted at two selected freeway segments with successive entrance or exit ramps in the City of Houston. Traffic simulation analysis results of these two case studies show that additional operational benefits can be achieved by extending an auxiliary lane beyond the freeway weaving segment. The study also found that if the weaving segment is followed by an entrance/exit ramp and this ramp has high traffic volume, it can be less operationally favorable to extend and terminate the auxiliary lane at this entrance/exit ramp location. Instead, dropping the auxiliary lane before this entrance/exit ramp represents a more operationally effective option.     

Establishing speed–flow–density relationships for exclusive motorcycle lanes

Abstract

The motorcycle is a popular mode of transport in Malaysia and developing Asian countries, but its significant representation in the traffic mix results in high rates of motorcycle accidents. As a result, the Malaysian Government decided to segregate motorcycle traffic along its new federal roads as an engineering approach to reduce accidents. However, traffic engineers needed to know the maximum traffic a motorcycle lane could accommodate. Despite substantial literature related to speed–flow–density relationships and capacities of various transport facilities, there is a knowledge gap regarding motorcycle lanes. This paper establishes motorcycle speed–flow–density relationships and capacities of exclusive motorcycle lanes in Malaysia. Observations of motorcycle flows and speeds were conducted along existing and experimental motorcycle lanes. Motorcycle speed–density data were aggregated and plotted for two types of observable motorcycle riding behaviour patterns that were influenced by the widths of a motorcycle lane: the headway pattern (lane width ≤ 1.7 m) and the space pattern (lane width > 1.7 m). For both riding patterns, regression analysis of motorcycle speed–density data best fits the logarithmic model and consequently the motorcycle flow–density and speed–flow models are derived. Motorcycle lane capacities for headway and space riding patterns are estimated as 3300 mc/hr/lane and 2200 mc/hr/m, respectively.     

基于元胞自动机的双车道变道交通流特性研究

在庞杂的城市交通环境下,驾驶员为了寻求更快的速度,常常采用主动的换道行为。由于汽车使用量逐年增长,换道引起的交通事故经常发生。研究车辆变道行为,寻求有效措施减少交通事故的发生,对提高道路安全性具有积极的意义。本文以多车道系统中车辆变道行为为研究对象,以元胞自动机理论为基础,对比分析单向单车道、单向双车道换道行为,并运用MATLAB仿真软件进行分析,获得变道交通流的相关特性曲线。     

Integrated optimization of transit priority operation at isolated intersections: A person-capacity-based approach

In this paper, a person-capacity-based optimization method for the integrated design of lane markings, exclusive bus lanes, and passive bus priority signal settings for isolated intersections is developed. Two traffic modes, passenger cars and buses, have been considered in a unified framework. Person capacity maximization has been used as an objective for the integrated optimization method. This problem has been formulated as a Binary Mixed Integer Linear Program (BMILP) that can be solved by a standard branch-and-bound routine. Variables including, allocation of lanes for different passenger car movements (e.g., left turn lanes or right turn lanes), exclusive bus lanes, and passive bus priority signal timings can be optimized simultaneously by the proposed model. A set of constraints have been set up to ensure feasibility and safety of the resulting optimal lane markings and signal settings. Numerical examples and simulation results have been provided to demonstrate the effectiveness of the proposed person-capacity-based optimization method. The results of extensive sensitivity analyses of the bus ratio, bus occupancy, and maximum degree of saturation of exclusive bus lanes have been presented to show the performance and applicable domain of the proposed model under different composition of inputs.     

Evaluating the effects of automated vehicle technology on the capacity of freeway weaving sections

Weaving sections, where a merge and a diverge are in close proximity, are considered as crucial bottlenecks in the highway network. Lane changes happen frequently in such sections, leading to a reduced capacity and the traffic phenomenon known as capacity drop. This paper studies how the emerging automated vehicle technology can improve the operations and increase the capacity of weaving sections. We propose an efficient yet effective multiclass hybrid model that considers two aspects of this technology in scenarios with various penetration rates: (i) the potential to control the desired lane change decisions of automated vehicles, which is represented in a macroscopic manner as the distribution of lane change positions, and (ii) the lower reaction time associated with automated vehicles that can reduce headways and the required gaps for lane changing maneuvers. The proposed model is successfully calibrated and validated with empirical observations from conventional vehicles at a weaving section near the city of Basel, Switzerland. It is able to replicate traffic dynamics in weaving sections including the capacity drop. This model is then applied in a simulation-based optimization framework that searches for the optimal distribution of the desired lane change positions to maximize the capacity of weaving sections. Simulation results show that by optimizing the distribution of the desired lane change positions, the capacity of the studied weaving section can increase up to 15%. The results also indicate that if the reaction time is considered as well, there is an additional combined effect that can further increase the capacity. Overall, the results show the great potential of the automated vehicle technology for increasing the capacity of weaving sections.     

Driving behaviour at motorway ramps and weaving segments based on empirical trajectory data

In the vicinity of ramps, drivers make route choices, change lanes and in most cases also adjust their speeds. This can trigger anticipatory behaviour by the surrounding vehicles, which are also reflected in lane changes and/or changes in speed. This phenomenon is called turbulence and is widely recognised by the scientific literature and various design guidelines. However the knowledge about the characteristics of turbulence is limited. This study investigates the microscopic characteristics of driving behaviour around 14 different on-ramps (3), off-ramps (3) and weaving segments (8) in The Netherlands, based on unique empirical trajectory data collected from a video camera mounted underneath a hovering helicopter. The data analysis reveals that lane changes caused by merging and diverging vehicles create most turbulence, that an increase in the amount of traffic results in a higher level of turbulence and that an increase in the available length for merging and diverging results in a lower level of turbulence. The results of this study are useful for improving the road design guidelines and for modelling driving behaviour more realistically.