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The objective of this paper is to analyze headway and speed distribution based on driver characteristics and work zone (WZ) configurations by utilizing Naturalistic Driving Study (NDS) data. The NDS database provides a unique opportunity to study car-following behaviors for different driver types in various WZ configurations, which cannot be achieved from traditional field data collection. The complete NDS WZ trip data of 200 traversals and 103 individuals, including time-series data, forward-view videos, radar data, and driver characteristics, was collected at four WZ configurations, which encompasses nearly 1,100 vehicle miles traveled, 19 vehicle hours driven, and over 675,000 data points at 0.1 s intervals. First, the time headway selections were analyzed with driver characteristics such as the driver’s gender, age group, and risk perceptions to develop the headway selection table. Further, the speed profiles for different WZ configurations were established to explore the speed distribution and speed change. The best-fitted curves of time headway and speed distributions were estimated by the generalized additive model (GAM). The change point detection method was used to identify where significant changes in mean and variance of speeds occur. The results concluded that NDS data can be used to improve car-following models at WZs that have been implemented in current WZ planning and simulation tools by considering different headway distributions based on driver characteristics and their speed profiles while traversing the entire WZ.

In fire evacuation situations, at corridors, many evacuees are plagued by high density, low velocity, and long waiting time. Therefore, engineers have to consider the countermeasure preventing crowd accidents. For this purpose, the development of pedestrian simulators that are constructed with concrete physical parameters, such as the headway distance between pedestrians, velocity, and specific flow, is required. To acquire the evacuation behavior in corridors, we conducted well-controlled pedestrian walking experiments in a confined corridor with realistic architectural geometry and modeled the pedestrian behaviors. An experimental loop corridor was constructed to acquire stable pedestrian flows without distractions from bottlenecks or merging flows. We conducted five experiments with different density patterns with an average density ranging from 1.28 to 3.42 people/m2 and a maximum of 96 test participants. We found that when the headway distance is 0.55–1.15 m, the velocity increases linearly with increasing headway distance, similar to single-file experiments. When the density is higher than 2.35 people/m2, the pedestrians cannot walk at a constant speed, and they exhibit stop-and-go behavior. In this situation, the percentage of pedestrians who walk at a headway distance of approximately 0.4–0.5 m, which is the minimum headway distance, increases. In addition, the fundamental diagram between density and velocity is acquired at a density higher than 1.4 people/m2 as an inversely proportional function. The density dependence on the specific flow is a linear function. The maximum specific flow is acquired at the marginal minimum density where a linear relationship is maintained.

Although the implementation of a new Automatic Train Protection (ATP) system increases the safety level, it also exerts some impact on the blocking time in block sections, and consequently, on the headway between trains. At the same time, ATP systems introduce a train positioning system based on odometry calculation and reporting back to the trackside system. This paper describes the concept of using virtual blocks based on train position reporting in the ATP system for purposes of non-occupancy determination. Virtual blocks can be used to reduce headways on railway lines without increasing the number of trackside signalling devices. Preliminary capacity assessment was performed to calculate the average headway depending on the signalling system with reference to a case study.

In this study, the relation between macroscopic parameters (speed, flow, density, occupancy) and microscopic parameters (lateral clearance, average gap, space headway, lateral movement duration, ov...

Driving simulator (DS) can be introduced as a popular tool in the transport industry for the analysis of driving behavior. Simulators should be validated in term of each specific recorded data, separately. When DSs are the source of data, each part of data which indicates a maneuver or a behavior should be validated separately. This paper is dedicated to the validation of car-following distance in the presence of considerable traffic flow which may increase mental load. In general, the results showed that DSs could be used for determining the behaviors of a driver by focusing on headway and car-following behavior. The distance kept by male drivers was shorter than female participants. It was seen that drivers between the ages of 20–40 were suitable for car-following tests in the DS by considering all real driving tasks in a congested traffic condition. Car-following threshold headway was less than 2 s for 85% of the participants, which is quite similar to the past results from real data in Iran. The average car-following headway threshold in the DS was 0.8 s, which is less than field data (1.2 s).

Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.

The effects of a package intervention including prompts, goal setting, feedback, education, and behavioral self-monitoring to increase following headway (decrease tailgating) of three young drivers...

The paper presents a new methodology for computing e-bikes equivalents at urban signalized intersections to meet the road spatial demand of e-bikes. By analyzing the measured traffic data of two-phase and four-phase signalized intersections, it is determined that the delay method and the headway method are not applicable to the e-bike Passage Car Equipment (PCE) calculation at intersections. An improved PCE calculation model based on the number of conflict events, speed and traffic volume calculated was established. It is determined that the PCE of left-turning, straight-through and right-turning of e-bikes at two-phase and four-phase signalized intersections are 0.27, 0.34, 0.25 and 0.29, 0.34, 0.27 respectively, and that the variation trend of PCE each turning directions at intersections is different from that of motor vehicles. By analyzing the generation mechanism of the PCE values, it is found that the proposed PCE values of each turning directions have changed reasonably. Compared to the pre-improved model, the traffic capacity calculated by the PCE proposed in this paper is found to be closer to the actual capacities.

Randomness affecting the operation of public transport systems generates significant increments in waiting times. A strategy to deal with this randomness is bus injection, in which buses are kept in specific points along the route ready to be dispatched when an event such as an extremely long headway occurs. In this work, a stochastic model based on the second moment of the headways distribution is developed to determine if one or more buses are worth reserving for injection in a public transport service. A single stop approach is initially used to determine an expression for the optimal headway threshold triggering the injection. Then, a model for the complete service is developed and used to determine when the empty bus should be injected within the headway once the decision to inject it has been taken. We show that the bus should be injected approximately when 57% of the headway has passed. Simulations with real data are used to test the proposed model, proving its accuracy in terms of measuring the impact on waiting times. The results show that reserving a bus to be injected can be better than operating the entire fleet continuously.

The calibration results of transport simulation model depend on selected parameters and their values. The aim of the present paper is to calibrate a transport simulation model by a two-step cluster analysis procedure to improve the reliability of simulation model results. Two global parameters have been considered: headway and simulation step. Normal, uniform and exponential headway generation models have been selected for headway. Application of two-step cluster analysis procedure to the calibration procedure has allowed reducing time needed for simulation step and headway generation model value selection.

The present paper aims to be useful for public transport operators and municipalities, as it should enable them to make decisions and to optimize public transport schedules during peak hours. In this study, we outline the data and the means necessary for the creation and use of a specific database for a dynamic spatial analysis of the public transportation network. This will facilitate the analysis of public transport vehicle operating programs and the simulation of new transport programs using open-source software. This paper delivers the first digital map of the public transport in Bucharest. Using the QGIS software and the PostgresSQL database, (i) we analyzed the accessibility of public transport stops for residential areas (5-min isochrones, corresponding to walking distances of 400 m), and (ii) we determined the correlation of transport vehicle programs with the existing transport network to optimize the headway of vehicles. These two elements were considered for the analysis of public transport performance. The research study was based on the tram network in Bucharest, but it can be easily upscaled for the entire public transport network and may be replicated in other large cities.

In this paper, an approach for longitudinal vehicle control is proposed that integrates a Machine Learning model for the speed prediction of the leading vehicle in order to improve the energy efficiency of the control in a city driving environment. The prediction is employed in an additional control mode that extends a conventional state-of-the-art speed and headway control. The approach aims to reduce the vehicle consumption through a more anticipatory driving style. The prediction model uses an encoder-decoder LSTM network with additional information from Vehicle-2-X communication and is trained through supervised learning with training data generated from simulation. A co-simulation environment comprising of an ego vehicle simulation and a microscopic traffic simulation is used for the generation of training data and the assessment of the control approach. The proposed control is compared to a benchmark Adaptive Cruise Control scheme for three battery electric vehicles with different powertrain specifications on multiple routes in a simulated city-driving environment of Darmstadt, Germany. The results show that the consumption of the vehicles can be reduced for all vehicles by 3–5 % while still maintaining similar mean speeds on different routes through the city.

This research proposed an analytical model of mixed flow consisting of Cooperative Adaptive Cruise Control (CACC) equipped vehicles and Human-driven Vehicles (HVs) in freeway merging areas. It quantifies the impact of CACC platoon on maximum throughput during the merge. The model describes the impedance of CACC platoon on lane change maneuvers together with its positive effect on maximum throughput due to shorter headway. To validate the effectiveness of the proposed model, a VISSIM based microscopic simulation evaluation is performed. The results confirm that the accuracy of the proposed model is over 80%. Sensitivity analysis is conducted in terms of various penetration rates of CACC equipped vehicles and v/c ratio levels. The proposed model demonstrates a consistent performance across all penetration rates and v/c ratio levels. The proposed model provides the foundation for future CACC-based traffic management strategies such as platooning strategy and dedicated lane management.

To solve the problems of bus bunching and large gaps, this study combines bus holding and speed adjusting to alleviate them respectively considering the characteristics of passenger’s perceived waiting time. The difference between passenger’s perceived waiting time at stops and actual time is described quantitatively through the expected waiting time of passengers. Bus holding based on a threshold method is implemented at any stops for bunching buses, and speed adjusting based on a Markovian decision model is implemented at limited stops for lagging buses. Simulations based on real data of a bus route show that the integrated control strategy is able to improve the service reliability and to decrease passengers’ perceived waiting time at stops. Several insights have been uncovered through performance analysis: (1) The increase of holding control strength results in improvement of the headway regularity, and leads to a greater perceived waiting time though, (2) Compared to traveling freely, suitable speed guidance will not slow down the average cruising speed in the trip, (3) The scale of passenger demand and through passengers are the two key factors influencing whether a stop should be selected as a speed-adjusting control point.

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately. This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated. The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.

In the context of urban traffic increasing, the study of driving behaviour in the vicinity of signalised intersection is emerging. Driving behaviour on the signalised road is different from the one on the highway due to the influence of signal lights. In this study, the authors analyse the trajectory data of Lankershim Boulevard from the next generation simulation (NGSIM) database. The car-following data are filtered to exclude the influences of lane changing. They analyse the driving data from both micro- and macro-perspectives. The quantitative relationship between acceleration and other factors, as well as the distribution of headway, are analysed. It is found that the statistic features of headway are in a discrepancy between different signal data. A simulation testbed is constructed, and two calibrated traditional car-following models are utilised to reproduce the driving behaviour at signalised intersections. The simulation results illustrate that the traditional models cannot be used to simulate traffic flow on the signalised road, i.e. they cannot describe the difference of headway under different signal states. Based on the data analyses, the authors propose a new car-following model with the consideration of signal status. The simulation results of the new model are in better agreement with the actual data.

Luggage-laden pedestrian flow is common at traffic terminals, but the impact of luggage on pedestrians has not been systematically investigated. In this study, experiment of one-dimensional luggage-laden pedestrian movement was performed, and analysis was carried out on pedestrian walking speed, acceleration, spatial headway distance, and fundamental diagram. For luggage type of bag and trolley case, it was found that luggage has little influence on the pedestrian walking speed, but the average distance headway of pedestrians was obviously enlarged. Luggage was observed to reduce the acceleration of the flow when pedestrians switched their movement status. In spite of results of normal walking and fast walking test case show difference, good consistency is reflected in the fundamental diagram. By comparison with previous studies on pedestrian fundamental diagrams, it was concluded that carrying luggage will make the stable point of pedestrian flow move further towards the lower density side of the fundamental diagram. This study provides valuable information about the impact of luggage and helps to develop the precise simulation models about luggage-laden pedestrian flow, which can finally contribute to the relevant pedestrian facility design.

Randomness affecting the operation of public transport systems generates significant increments in waiting times. A strategy to deal with this randomness is bus injection, in which buses are kept in specific points along the route ready to be dispatched when an event such as an extremely long headway occurs. In this work, a stochastic model based on the second moment of the headways distribution is developed to determine if one or more buses are worth reserving for injection in a public transport service. A single stop approach is initially used to determine an expression for the optimal headway threshold triggering the injection. Then, a model for the complete service is developed and used to determine when the empty bus should be injected within the headway once the decision to inject it has been taken. We show that the bus should be injected approximately when 57% of the headway has passed. Simulations with real data are used to test the proposed model, proving its accuracy in terms of measuring the impact on waiting times. The results show that reserving a bus to be injected can be better than operating the entire fleet continuously.

The precise and fast prediction of running times is extremely important in railway operations and management. This paper deals with the analytical calculation of railway running times. It contains a new method for the computation of the acceleration process, leading to a fast and compact simulation framework. The approach is compared to a common algorithm, the velocity micro-step Euler-method. Additionally, the accuracy of the new acceleration calculation method is also compared to empirical acceleration behavior of a standard commuter train in Germany. As a result, three suggestions for the application of the algorithm (the calculation of minimum headway times, energy consumption of a train, and energy-efficient driving in the context of track capacity) are shown.

One research area within the development of automated vehicles deals with the impact analysis on traffic flow by numerical simulation. This study investigates human drivers’ acceptance while interacting with different levels of automated vehicles on highways including on- and off-ramps. Reactions between conventional, human driven vehicles (CV) and automated vehicles (AV) were tested using a driving simulator. Gaps and headways between vehicles were recorded and analyzed. The analysis indicates similar behavior between CVs and aggressive AVs (short headways) while prudent AVs were perceived less favorable by the test drivers. Additionally, long headways showed more disturbance in traffic flow than shorter headway setups of the automatic distance control (ACC).

This paper reports on two studies focusing on the design of collision avoidance headway displays. The first study aims at examining interface issues relating to the headway information to drivers. It investigates integrated visual and auditory warnings. The purpose of the second study is to examine two particular factors that may affect drivers' braking responses in their use of an experimental Collision Avoidance System. It looks at the effects of both the absolute speeds of the target and following vehicle and the relative speed differences between the two vehicles.

Using a car-driving simulator created based on the open source engine---TORCS---we investigate human behavior in car-driving within the car-following setup. Five subjects with different skill of driving were involved in these experiments. Here we present the results of experiments comparing two particular versions of car-following setup; within one the lead car speed was fixed, within the other it changed in a certain irregular way. The latter setup was implemented for three different time scales of speed change representing "fast," "intermediate," and "slow" speed variations. In the case of "fast" speed variations a subject just cannot drive the car such that its speed and the lead car speed vary in time synchronically. In the case of "slow" speed variations it is an easy task. The histograms of the headway, velocity, acceleration, jerk, and the time derivative of pedal position (the main control parameter) constructed based on the collected data are compared. Previously we have demonstrated that driver actions should be categorized as human intermittent control. Based on the presented results we draw the conclusion that this type of driver actions is not sensitive to the uncertainty in the lead car motion.

Based on the data from two double-deck rail car egress experiments, presented project aims to estimate the effects of various parameters on the evacuation time. This paper focuses on a microscopic analysis of the time headways showing that several headway-based statistics may illustrate the macroscopic behavior as well as may explain observed phenomena. Applying this approach, we have concluded that the evacuation movement may be decomposed into two phases with different performance and that the efficiency of evacuation is given by their proportion. Moreover, we have observed that the total evacuation time is affected mainly by the frequency of extremely large headways, i.e. the performance significantly depends on few slowest persons.

This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow. We propose a Vehicle-in-the-Loop (VIL) testing environment in which experimental CAVs driven on a track interact with surrounding virtual traffic in real-time. We explore the energy savings when following city and highway drive cycles, as well as in emergent highway traffic created from microsimulations. Model predictive control handles high level velocity planning and benefits from communicated intentions of a preceding CAV or estimated probable motion of a preceding human driven vehicle. A combination of classical feedback control and data-driven nonlinear feedforward control of pedals achieve acceleration tracking at the low level. The controllers are implemented in ROS and energy is measured via calibrated OBD-II readings. We report up to 30% improved energy economy compared to realistically calibrated human driver car-following without sacrificing following headway., Comment: Preprint Submission to Transportation Research Part C, 27 Pages, 20 Figures

As the accident-prone sections and bottlenecks, highway weaving sections will become more complicated when it comes to the mixed-traffic environments with connected and automated vehicles (CAVs) and human-driven vehicles (HVs). In order to make CAVs accurately identify the driving behavior of manual-human vehicles to avoid traffic accidents caused by lane changing, it is necessary to analyze the characteristics of the mandatory lane-changing (MCL) process in the weaving area. An analytical MCL method based on the driver’s psychological characteristics is proposed in this study. Firstly, the driver’s MLC pressure concept was proposed by leading in the distance of the off-ramp. Then, the lane-changing intention was quantified by considering the driver’s MLC pressure and tendentiousness. Finally, based on the lane-changing intention and the headway distribution of the target lane, an MLC positions probability density model was proposed to describe the distribution characteristics of the lane-changing position. Through the NGSIM data verification, the lane-changing analysis models can objectively describe the vehicle lane-changing characteristics in the actual scenarios. Compared with the traditional lane-changing model, the proposed models are more interpretable and in line with the driving intention. The results show significant improvements in the lane-changing safe recognition of CAVs in heterogeneous traffic flow (both CAVs and HVs) in the future.

This paper investigates the problem of driver distraction detection using driving performance indicators from onboard kinematic measurements. First, naturalistic driving data from the integrated vehicle-based safety system program are processed, and cabin camera data are manually inspected to determine the driver’s state (i.e., distracted or attentive). Second, existing driving performance metrics, such as steering entropy, steering wheel reversal rate, and lane offset variance, are reviewed against the processed naturalistic driving data. Furthermore, a nonlinear autoregressive exogenous (NARX) driving model is developed to predict vehicle speed based on the range (distance headway), range rate, and speed history. For each driver, the NARX model is then trained on the attentive driving data. We show that the prediction error is correlated with driver distraction. Finally, two features, steering entropy and mean absolute speed prediction error from the NARX model are selected, and a support vector machine is trained to detect driving distraction. Prediction performances are reported.

This research aims to develop a simulation-based methodology in the estimation of saturation flow at signalized intersections under heterogeneous traffic conditions in India. Field surveys have been carried out particularly during peak periods at two signalized intersections, namely Vijay Char Rasta intersection situated in Ahmedabad and Rangila Park intersection situated in Surat. Videographic technique is employed to capture the data at macro- and microlevel. The videographic approach provides the queue building, moving, and queue dissipation at microlevel. The field data pertains to traffic volume, compositions, turning movements, headway, cycle time, and phasing at the approach. The survey also concentrates on noting the queue length of each approach. Apart from videographic survey, road inventory survey and spot speed surveys are also carried out in order to use this data in VISSIM 7.0 for the development of network and defining speed distributions. The present work mainly focused on the development of PCUs, dynamic saturation flows, VISSIM modeling and validation, regression modeling and validation, and sensitivity analysis. Passenger car unit for each vehicle category is calculated by two methods—headway method and optimization method. Saturation flow is estimated with the help of PCU values estimated by different methods, and these values are compared. TRL method is adopted in the estimation of saturation flow. A 5-s interval is considered in this method. Nearly, 8842 VPH is the saturation flow against to 5460 PCU of headway method and 5071 PCU of optimization method for 3 lane approach of Vijay Char Rasta intersection. Saturation flow is almost double in terms of mixed traffic. Therefore, saturation flow cannot be static in present study but is dynamic. Dynamic saturation flow values are provided for varied composition and approach widths. Base network is created in VISSIM 7.0. Model is calibrated as per Indian driving behavior for both Vijay Char Rasta and Rangila Park. Average queue length is considered for validating the model. Sensitivity analysis is carried out after validating the simulation model. It is observed that change in road width is having significant effect on saturation flow when compared to change in turning movements. Regression analysis is carried out for developing multilinear regression saturation flow model (MLR–SFM). The model is developed between saturation flow and proportion of 2W, 3W, small car, big car, and road width. 65–35 combination is observed to be best fitted combination in developing and validating the model. Similar to the observations in VISSIM modeling, it is observed that change in road width is having a significant effect on saturation flow when compared to change in traffic compositions.

The vision to integrate automated vehicles into manual traffic motivates to investigate automated merging in dense traffic. To gain an easy, distinct and interpretable behavior for interacting traffic this study investigates release conditions of lane changes into small gaps in a within-subject design on a test track with 39 participants. To generate standardized situations all merging maneuvers are performed automatically. The study is divided into two parts. In the first part participants validate different times headway between the participant’s vehicle and automated vehicle under different situational parameters (deceleration to target gap, velocity and existence of road work). In the second part, participants release the lane change of the automated vehicle themselves, when they expected it to merge. Here, in addition to part one, the automated vehicle adjusted velocity to the target gap with weak and strong deceleration. The results show that participants prefer an efficient lane change of the automatic vehicle, where interacting traffic has to react as little as possible. Compliance with safety distances is not decisive. The required times headway between automated and interacting vehicles decreases with higher velocity and in lane narrowing situations. The study contributes to the design of vehicle behaviour that can enhance the acceptance of automated vehicles in mixed-traffic.

In this paper, we introduce a train following model which can efficiently capture the effects of train interactions in an urban rail line. The train following model is based on the estimation of induced delay for a train closely following a lead train, and is derived from empirical analysis based on historical track circuit data. Based on an analysis of sequential train delays, a mesoscopic train state prediction model is developed which can be used to predict the behavior of the system with respect to changes in initial conditions (e.g. scheduled headway, headway variation or dwell time) and disruptions. The performance analysis using this train following model is richer and more accurate than that from analytical macroscopic models while not being as time- and resource- intensive as a detailed simulation model. The development and potential application of this model is demonstrated for the Massachusetts Bay Transportation Authority (MBTA) Red Line. The model can be used for both real-time control strategy evaluation and offline operational strategy analyses.

Saturation flow rate is the most important parameter required for the capacity analysis of signalised intersections. Under homogeneous traffic conditions, the concept of constant queue discharge headway is adopted for the estimation of saturation flow. But that concept could not be adopted under heterogeneous traffic conditions due to absence of lane discipline and wide variation in static and dynamic characteristics of vehicles. The variability in vehicle mix under heterogeneous traffic is usually accounted for in the estimation of saturation flow by using the widely accepted measure, Passenger Car Unit (PCU). But estimation of individual PCU values for different categories of vehicles under heterogeneous traffic conditions is a complex task due to its dependency on multitude of site specific factors. Hence in this study saturation flow in PCU/h is estimated with out resorting to individual PCU factors. Area occupancy, a surrogate measure of density and a measure of proportion of time vehicle area is present in observation region, is taken as the basis for the estimation of saturation flow rate in PCU/h. Area occupancy of the heterogeneous traffic stream during saturated green time is equated to standard car occupancy and the number of standard car spaces are determined. Saturation flow in PCU/h is then estimated based on number of standard car spaces. A mathematical model is developed for the prediction of saturation flow for approaches that satisfy the base conditions set in the present study. Effect of right turn movements on saturation flow rate is also explored. Turning vehicles tend to reduce the departure efficiency of through moving vehicles due to the turning manoeuvre in combined movements. Adjustment factor on saturation flow rate is proposed to account for the influence of right turn traffic that is given right of way in same phase along with through traffic.

Due to the individuals’ interactions with the physical environment, pedestrian walking on various stair-facilities may present different features. Performing controlled pedestrian experiments have been regarded as a reliable data provision approach in this field. With this method, a series of pedestrian single-file experiments on specific stair configurations has been organized. Based on the individual’s trajectories information extracted from eight well-structured stair scenarios, this study investigates a range of questions concerning the effects of stair geometry on pedestrian locomotion properties. This including the influence of stair tread depth, riser height and stair slope as well as pedestrian dynamics on micro and macro levels. Our analysis indicated that there is a unified behavioral mechanism between pedestrian single-file movement on horizontal plane and stairs, i.e., the two regimes of speed-headway relationship. Besides, our analysis displayed those pedestrian characteristics, i.e., free movement speed and free movement headway varies as a result of different stair configurations. Therefore, we developed a linear regression model that predicts the free walking speed based on tread depth and riser height of the stair. Our results also indicated the significant effect of stair gradient on pedestrian density-specific flow relation on stairs by the microscope fundamental diagram method. We found the data point of density-specific flow for pedestrians almost has the same shape despite their walking in different facilities. The data and findings could contribute to the design of large buildings or stations, upgrade and enhance microscope pedestrian simulation models to provide more reality outputs of the human walking on stairs.

The projected rapid growth of the market penetration of connected and autonomous vehicle technologies (CAV) highlights the need for preparing sufficient highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). This study proposes an analytical capacity model for highway mixed traffic based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. This analytical framework further enables us to build a compact lane management model to efficiently determine the optimal number of dedicated CAV lanes to maximize mixed traffic throughput of a multi-lane highway segment. This optimization model addresses varying demand levels, market penetration rates, platooning intensities and technology scenarios. The model structure is examined from a theoretical perspective and an analytical approach is identified to solve the optimal CAV lane number at certain common headway settings. Numerical analyses illustrate the application of this lane management model and draw insights into how the key parameters affect the optimal CAV lane solution and the corresponding optimal capacity. This model can serve as a useful and simple decision tool for near future CAV lane management.

This paper proposes a bi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced.

This study establishes a multi-objective optimization model for the capacity allocation of an urban rail transit network based on multi-source data on the Beijing metro passenger flow. The model considers the operating costs of trains and the expenses related to the waiting time of transferring passengers. The model constraints include the distribution characteristics of passenger flow, headway, load factor, and available trains. The capacity allocation scheme for 16 railway lines was obtained by adopting a model of the Beijing rail transit network and its passenger flow. We also analyzed the frequency at which Line 4 is reduced from 55 to 45 and the frequency at which Line 10 is reduced from 60 to 52 if a short turn is adopted. In addition, when the upper limit of the load factor increased from 60 to 70%, the operational costs were reduced by 4.6%, while the total passenger waiting time increased by 1%. The transfer costs changed the capacity optimization and allocation scheme, and the proportion of the transfer cost among the total costs increased when the time value increased.

Chinese high-speed railways faced a fast development in recent years. Their performances are still confronted with disruptions unavoidably, which impact on the reliability of the traffic and passenger satisfaction. This paper presents a rescheduling model which is able to solve the critical problem of effective disruption management (namely, fast and dynamic train speed adaptation, supervision of braking and changing train sequence due to incidents, warnings or alarms), and consider in detail the signalling and safety systems based on a quasi-moving block system with variable headways. We integrate the modelling of efficient traffic management measures and the supervision of speed, braking and headway in one general job-shop model. We use a commercial solver with a custom-designed two-step method to speed up the procedure in order to solve instances from real-world high-speed networks in China quickly. Overall, the approach guarantees the resolution of the traffic control and speed management within few minutes of computation time. The output demonstrates that the proposed approach can achieve a reduction of train delays by 70% compared to the solution determined by keeping the order of the original timetable, and get the optimality for more than 90% of instances with a realistic case.

In transportation terminals, a lot of passengers usually travel with luggage, which affects not only their mobility but also the ones who have no luggage, especially on stairs. As a result, congestion could be severer during rush hours. However, the impact of luggage on the movement characteristics of pedestrians ascending and descending stairs have not been systematically studied yet. Therefore, in this study, a series of single-file luggage-laden movement experiments under controlled laboratory conditions were performed. Trajectories have been extracted to analyze the movement features under different luggage-laden ratio scenarios. From the space–time diagram, it can be found the average stopping time gradually increases as the increase of the luggage-laden ratios. In the relationship between spatial headway and speed, two regimes are found, i.e., free regime and strongly constrained regime. The minimum descending headway is basically the same as the tread depth, while the minimum ascending headway is greater than the tread depth. The pedestrian adaptation time in the scenario of a high luggage-laden ratio is longer than that of none. Finally, in addition to longitudinal movement, with the increase of speed, pedestrian lateral sway amplitude decline rate in the descending process is faster than that in the ascending process. The impact of carrying luggage ascent and descent the stairs on pedestrian movement characteristics provided in this study is helpful not only for the calibration and verification of the pedestrian models but also for the design of pedestrian facilities in transportation terminals.

This study proposes a new car-following model considering the effects of the electronic throttle dynamics to capture the characteristics of connected autonomous vehicular traffic flow without lane discipline. In particular, the proposed model incorporates the effects of both electronic throttle opening angle and lateral gap into the traffic flow model by assuming that the information on electronic throttle dynamics is shared by surrounding vehicles through vehicle-to-vehicle communications. Stability of the proposed model is analyzed using the perturbation method. Numerical experiments analyze three scenarios: start, stop and evolution processes for the scenarios of lane-discipline-based full velocity difference (FVD) model, non-lane-based full velocity difference car-following (NLBCF) model and non-lane-discipline and throttle-based car-following model, respectively. Results from numerical experiments illustrate that the proposed car-following model has a larger stale region compared with the FVD and NLBCF models. In addition, it also demonstrates that the proposed car-following model can better represent the characteristics of connected and autonomous vehicular traffic flow in terms of the responsiveness, smoothness and stability with respect to the position, velocity, acceleration/deceleration and space headway profiles.

Driver distraction is a growing and pervasive issue that requires multiple solutions. Voice-recognition (V-R) systems may decrease the visual-manual (V-M) demands of a wide range of in-vehicle system and smartphone interactions. However, the degree that V-R systems integrated into vehicles or available in mobile phone applications affect driver distraction is incompletely understood. A comprehensive meta-analysis of experimental studies was conducted to address this knowledge gap. To meet study inclusion criteria, drivers had to interact with a V-R system while driving and doing everyday V-R tasks such as dialing, initiating a call, texting, emailing, destination entry or music selection. Coded dependent variables included detection, reaction time, lateral position, speed and headway. Comparisons of V-R systems with baseline driving and/or a V-M condition were also coded. Of 817 identified citations, 43 studies involving 2000 drivers and 183 effect sizes (r) were analyzed in the meta-analysis. Compared to baseline, driving while interacting with a V-R system is associated with increases in reaction time and lane positioning, and decreases in detection. When V-M systems were compared to V-R systems, drivers had slightly better performance with the latter system on reaction time, lane positioning and headway. Although V-R systems have some driving performance advantages over V-M systems, they have a distraction cost relative to driving without any system at all. The pattern of results indicates that V-R systems impose moderate distraction costs on driving. In addition, drivers minimally engage in compensatory performance adjustments such as reducing speed and increasing headway while using V-R systems. Implications of the results for theory, design guidelines and future research are discussed.

This paper presents a multi-modal framework for modelling and analysing different headway control strategies for improving bus service regularity. The interaction between buses and their surroundin...

Bus headway regularity heavily affects transit riders’ attitude for choosing public transportation and also serves as an important indicator for transit performance evaluation. Therefore, an accurate estimate of bus headway can benefit both transit riders and transit operators. This paper proposed a relevance vector machine (RVM) algorithm to predict bus headway by incorporating the time series of bus headways, travel time, and passenger demand at previous stops. Different from traditional computational intelligence approaches, RVM can output the probabilistic prediction result, in which the upper and lower bounds of a predicted headway within a certain probability are yielded. An empirical experiment with two bus routes in Beijing, China, is utilized to confirm the high precision and strong robustness of the proposed model. Five algorithms [support vector machine (SVM), genetic algorithm SVM, Kalman filter, k-nearest neighbor, and artificial neural network] are used for comparison with the RVM model and the result indicates that RVM outperforms these algorithms in terms of accuracy and confidence intervals. When the confidence level is set to 95%, more than 95% of actual bus headways fall within the prediction bands. With the probabilistic bus headway prediction information, transit riders can better schedule their trips to avoid late and early arrivals at bus stops, while transit operators can adopt the targeted correction actions to maintain regular headway for bus bunching prevention.

A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway and number of stops, and speed data are used to estimate traffic density. This paper reveals that, with 20% and greater CV penetration levels, the accuracy of the density information with the AI-aided CVT is a minimum of 85%. Moreover, this paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels. Level of service (LOS) is the indicator of traffic congestion level on highways and is described with traffic density in terms of passenger car/mile/lane for a specific free flow speed. LOS estimated using the CVT-AI density estimation method is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors. With a 10% or more CV penetration, higher accuracy is achieved using the CVT-AI algorithm compared with the PeMS density estimation algorithm.

The aim of this simulator study was to determine whether the effects of fog on driver behaviour were identical for a given road type and whether they could explain fog-related crashes according to road type. Thirty-three participants drove on both two-lane rural roads and motorways according to three visibility conditions (clear weather, 60 m-visibility and 30 m-visibility) and two driving situations (non-free driving and free driving). The variables were: Speeds (Ss), Headway Distances (HDs) and Headway Times (HTs). Fog was simulated using special software designed as part of the French Predit project VOIR, allowing both realistic vehicle headlights and halos to be displayed. The results showed that the drivers decreased their speed with decreasing visibility distance, i.e., speeds were slower in the 30 m-visibility conditions than in clear conditions; but, speeds on the two-lane motorway remained faster than on the two-lane rural road, even for the denser fog. In the 30 m-visibility condition, the faster speeds driven on motorways than on two-lane rural sections violated those advocated by the French Highway Code. The distances travelled in conjunction with the speeds driven according to the two-second rule revealed that HTs less than 2 s and small HDs do not necessarily match with hazardous driving.; Le but de cette étude sur le simulateur était de déterminer si les effets du brouillard sur le comportement du conducteur étaient identiques pour un type de route donné et s'ils pouvaient expliquer les collisions liées au brouillard selon le type de route. Trente-trois participants ont conduit sur des routes rurales à deux voies et des autoroutes selon trois conditions de visibilité (temps clair, 60 m de visibilité et 30 m de visibilité) et deux situations de conduite (conduite réglementée et conduite libre). Les variables étaient: Vitesses (Ss), Distances de progression (HD) et temps de progression (HTs). Le brouillard a été simulé à l'aide d'un logiciel spécial conçu dans le cadre du projet PREdit français VOIR, permettant d'afficher les phares et les halos de Îhicules réalistes. Les résultats ont montré que les conducteurs ont diminué leur vitesse avec une distance de visibilité décroissante, c'est-à-dire que les vitesses étaient plus lentes dans les conditions de visibilité de 30 m que dans des conditions claires; mais, les vitesses sur l'autoroute à deux voies sont restées plus rapides que sur la route rurale à deux voies, même pour le brouillard plus dense. Dans la condition de 30 m de visibilité, les vitesses plus rapides sur les autoroutes que sur les zones rurales à deux voies ont violé celles préconisées par le Code de la route. Les distances parcourues en conjonction avec les vitesses conduites selon la règle des deux secondes ont réÎlé que les HT de moins de 2 s et les petites HD ne correspondent pas nécessairement à une conduite dangereuse.

This paper describes a connected-vehicle-based system architecture which can provide more precise and comprehensive information on bus movements and passenger status. Then a dynamic control method is proposed using connected vehicle data. Traditionally, the bus bunching problem has been formulated into one of two types of optimization problem. The first uses total passenger time cost as the objective function and capacity, safe headway, and other factors as constraints. Due to the large number of scenarios considered, this type of framework is inefficient for real-time implementation. The other type uses headway adherence as the objective and applies a feedback control framework to minimize headway variations. Due to the simplicity in the formulation and solution algorithms, the headway-based models are more suitable for real-time transit operations. However, the headway-based feedback control framework proposed in the literature still assumes homogeneous conditions at all bus stations, and does n...

Red-light-running (RLR) emerges as a major cause that may lead to intersection-related crashes and endanger intersection safety. To reduce RLR violations, it’s critical to identify the influential factors associated with RLR and estimate RLR frequency. Without resorting to video camera recordings, this study investigates this important issue by utilizing high-resolution traffic and signal event data collected from loop detectors at five intersections on Trunk Highway 55, Minneapolis, MN. First, a simple method is proposed to identify RLR by fully utilizing the information obtained from stop bar detectors, downstream entrance detectors and advance detectors. Using 12 months of event data, a total of 6550 RLR cases were identified. According to a definition of RLR frequency as the conditional probability of RLR on a certain traffic or signal condition (veh/1000veh), the relationships between RLR frequency and some influential factors including arriving time at advance detector, approaching speed, headway, gap to the preceding vehicle on adjacent lane, cycle length, geometric characteristics and even snowing weather were empirically investigated. Statistical analysis shows good agreement with the traffic engineering practice, e.g., RLR is most likely to occur on weekdays during peak periods under large traffic demands and longer signal cycles, and a total of 95.24% RLR events occurred within the first 1.5 s after the onset of red phase. The findings confirmed that vehicles tend to run the red light when they are close to intersection during phase transition, and the vehicles following the leading vehicle with short headways also likely run the red light. Last, a simplified nonlinear regression model is proposed to estimate RLR frequency based on the data from advance detector. The study is expected to helpbetter understand RLR occurrence and further contribute to the future improvement of intersection safety.

This paper analyzes and compares two different relative spatial position (RSP) designs in an integrated e-hailing/fixed-route transit system: a zone-based design that operates e-hailing vehicles within a zone, and a line-based design that operates e-hailing vehicles along a fixed-route transit line and with a stable headway. To conduct a meaningful comparison, the optimal design problems for both systems are formulated using a same analytical framework based on the continuous approximation approach. A comprehensive numerical experiment is performed to compare various cost components corresponding to the optimal designs, and a discrete-event simulation model is developed to validate the analysis. The analytical and simulation results agree with each other well, with a discrepancy in the total system cost less than 5% in most test scenarios. These results also suggest that the line-based system consistently outperforms the zone-based system in terms of both agency and user costs, for all scenarios tested. Compared to the zone-based design, the line-based design features a sparser fixed-route network (resulting in larger stop spacing) but a higher dispatching frequency. It is concluded that the higher efficiency of the line-based design is likely derived from the strategy of operating e-hailing vehicles with a more regular route/headway structure and allowing ride-sharing.

Due to their different sizes and operational characteristics, vehicles other than passenger cars have a different influence on traffic operations especially at intersections. The passenger car equivalent (PCE) is the parameter that shows how many passenger cars must be substituted for a specific heavy vehicle to represent its influence on traffic operation. PCE is commonly estimated using headway-based methods that consider the excess headway utilized by heavy vehicles. In this research, the PCE was estimated based on the delay parameter at three signalized intersections in Tehran, Iran. The data collected were traffic volume, travel time for each movement, signalization, and geometric design information. These data were analysed and three different models, one for each intersection, were constructed and calibrated using TRAF-NETSIM simulation software for unsaturated traffic conditions. PCE was estimated under different scenarios and the number of approach movements at each intersection. The results showed that for approaches with only one movement, PCE varies from 1.1 to 1.65. Similarly, for approaches with two and three movements, the PCE varies from 1.07 to 1.99 and from 0.76 to 3.6, respectively. In addition, a general model was developed for predicting PCE for intersections with all of the movements considered. The results obtained from this model showed that the average PCE of 1.5 is similar to the value recommended by the HCM (Highway Capacity Manual) 1985. However, the predicted PCE value of 1.9 for saturated threshold is closer to the PCE value of 2 which was recommended by the HCM 2000 and HCM 2010.