Search

An extension of a recent framework for analyzing scheduling disutility with perception errors is derived. In such framework, the traveler has (α,β,γ)scheduling preferences. Although the actual travel time of travelers is influenced by departure time, travelers make a route-choice decision on the basis of their perceived travel time. Thus, we mainly investigate the effects of perception errors on the optimal departure time. The optimal departure time and expected disutility are obtained on the basis of the proposed scheduling disutility model with perception errors. The inverse function curve of a standardized normal distribution is introduced to geometrically analyze the positive correlation between perception errors and optimal departure time. Our empirical example further illustrates that departure time as an endogenous variable affects the expectation and variance of perceived travel time and implies that perception errors are related to departure time. The linear relationship between the mean and variance of perceived travel time and the departure time is analyzed through an empirical illustration. The expected optimal disutility and departure time are derived, provided that the mean and variance of perception errors and departure time satisfy linear dependence. Finally, the results of the analysis of scheduling services with standardized normal distributions agree with the theoretical results and linear assumption in this study.

A vehicular communication system can provide motion information based on various sensors. Thus, adaptive cruise control (ACC) systems based on a reliable communication system can relieve traffic congestion in a platoon. However, acquiring vehicle information entails uncertainties because of the disturbance of traffic environment and vehicular communication systems, thereby influencing vehicle control performance. In this study, the desired safety margin (DSM) model is employed to investigate the influence of uncertainty on car-following performance, such as starting, emergency braking, and car-following processes. Based on the DSM model, the disturbance level on perceived safety margin is introduced to characterize the uncertainty of vehicle information. The stability criterion of the DSM model with the disturbance level is derived via linear stability theory. Analytical results indicate that a negative value of disturbance level will enlarge the stable region. By contrast, a positive value is conducive for maintaining the consensus state and achieving the high acceleration and deceleration of following vehicles in the starting process. Findings show that the disturbance in the vehicular system significantly influences car-following performance. To enhance the smoothness and stability of traffic flow evolution, a new control strategy is proposed in this study. Numerical experiments illustrate the effectiveness of the proposed control strategy in stabilizing traffic flow. This study highlights the need to resolve the stability of sensors and vehicular communication systems, and to develop the vehicular controller to help ACC systems improve vehicle control performance in the car-following process.

In this paper, an extended desired safety margin (DSM) car-following model is proposed by accounting for the effect of the variation of historical perceived risk and acceptable risk in terms of the DSM model. By conducting gray correlation analysis, an investigation is carried out into whether the variation of historical perceived risk and acceptable risk has important effects on the acceleration or deceleration of a target vehicle based on a real-vehicle test platform. Through simulated results, the dynamic performance of an extended DSM model is investigated in comparison with the DSM model. Conclusions show that the extended DSM model can better reflect the characteristics of traffic flow compared with the DSM model, and can improve the performance of the vehicle in the start, stop, and car-following processes. Numerical simulations further demonstrate that the extended DSM model can improve traffic safety via the changes of time-to-collision and safety margin when external disturbance is introduced into the leading vehicle. Thus, historical information about target vehicles should be considered to improve the performance of car-following in adaptive cruise control (ACC) and automotive platoon driving.

In the dynamic interaction of a driver-vehicle-environment system, risk perception of drivers changes dynamically, having significant impacts on driving behavior and vehicles movement. In China, because there is less construction of stop signs, as well as limited regulation of driving courtesy, traffic operation and safety issues at unsignalized intersections require harder concern. Thus, in this study, focus was on risk perception of drivers at unsignalized intersections in China and then analysis of vehicles movement with consideration of drivers’ risk perception. A total of 150 typical merging cases were selected at an unsignalized intersection in Kunming City. On the basis of cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established. Drivers’ acceptable risk perception levels were identified by using a self-developed data analysis method. On the basis of game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed; then the vehicles merging movement model was established. Finally, the vehicles merging movement model was validated by using data collected from real-world vehicle movements and driver decisions. Results showed that the developed vehicles movement model had both high accuracy and good applicability. This study could provide theoretical and algorithmic references for the microscopic simulation and vehicle active safety control system.

In the dynamic interaction of a driver-vehicle-environment system, risk perception of drivers changes dynamically, having significant impacts on driving behavior and vehicles movement. In China, because there is less construction of stop signs, as well as limited regulation of driving courtesy, traffic operation and safety issues at unsignalized intersections require harder concern. Thus, in this study, focus was on risk perception of drivers at unsignalized intersections in China and then analysis of vehicles movement with consideration of drivers’ risk perception. A total of 150 typical merging cases were selected at an unsignalized intersection in Kunming City. On the basis of cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established. Drivers’ acceptable risk perception levels were identified by using a self-developed data analysis method. On the basis of game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed; then the vehicles merging movement model was established. Finally, the vehicles merging movement model was validated by using data collected from real-world vehicle movements and driver decisions. Results showed that the developed vehicles movement model had both high accuracy and good applicability. This study could provide theoretical and algorithmic references for the microscopic simulation and vehicle active safety control system.

Forecasting of short-term traffic states on expressways by adopting spatial–temporal models has gained increasing attention. Traffic data from neighboring sites were demonstrated to provide valuable information for predicting traffic states at sites of interest. However, when one considers the need to analyze the multivariate nature of traffic states over spatial dimensions, as well as of different models for various times of day, the interaction effects between spatial–temporal patterns require further investigation. This study addressed this issue on a segment of Shanghai North–South Expressway. Temporal characteristics of traffic volumes and speeds were analyzed by dividing the time of day into ordinal periods with relatively stable states. Then, spatial vector autoregressive (VAR) models were constructed at typical analysis periods for volume and speed forecasting by considering different combinations of upstream and downstream impacts. The results showed that the impact of downstream traffic conditions on upstream traffic cannot be neglected, especially in peak periods. For off-peak periods, traffic states at a location largely depended on upstream states, while downstream states appeared to have fewer effects. In such cases, models incorporating only upstream states were proved able to achieve sufficient accuracy. In addition, encouraging forecasting results were found when VAR models were compared with traditional methods (e.g., autoregressive integrated moving average and historical average), which failed to consider the spatial component of spatial–temporal patterns. All analyses helped to demonstrate the applicability of VAR models and to provide practical guidance for incorporating spatial–temporal dynamics into forecasting of expressway traffic states.

Two types of delay variability have been commonly referred to at signalized intersections: arrival-time-dependent individual vehicle delay variability and average control delay variability. The former focuses on the delay experienced by individual vehicles, whereas the latter deals with the average consequence of signal control for the entire traffic flow during a given evaluation period. With a number of methods available for estimation, little research has been conducted to enable a comprehensive comparison of these two variability measures. This issue is addressed by a comparison of these two types of delay variability at isolated, fixed-time controlled intersections based on two analytical models accounting for the stochastic nature of traffic arrivals and overflow queues. The results show that in undersaturated conditions, individual vehicle delay tends to have a wider distribution and a larger variance compared with average control delay. In oversaturated conditions, the shapes of both types of delay distributions shift to the higher values with larger variances. Different arrival processes are found to have a significant influence on the pairwise delay distributions. In addition, the width of the delay distribution based on percentiles is used to quantify the delay uncertainty under different degrees of saturation. Compared with average control delay, individual vehicle delay is found to have a larger uncertainty in undersaturated conditions, whereas it becomes relatively certain in oversaturated conditions. All the analyses have special practical implications for enriched knowledge of stochastic arrival and departure processes at signals.

Two types of delay variability have been commonly referred to at signalized intersections: arrival-time-dependent individual vehicle delay variability and average control delay variability. The former focuses on the delay experienced by individual vehicles, whereas the latter deals with the average consequence of signal control for the entire traffic flow during a given evaluation period. With a number of methods available for estimation, little research has been conducted to enable a comprehensive comparison of these two variability measures. This issue is addressed by a comparison of these two types of delay variability at isolated, fixed-time controlled intersections based on two analytical models accounting for the stochastic nature of traffic arrivals and overflow queues. The results show that in undersaturated conditions, individual vehicle delay tends to have a wider distribution and a larger variance compared with average control delay. In oversaturated conditions, the shapes of both types of delay distributions shift to the higher values with larger variances. Different arrival processes are found to have a significant influence on the pairwise delay distributions. In addition, the width of the delay distribution based on percentiles is used to quantify the delay uncertainty under different degrees of saturation. Compared with average control delay, individual vehicle delay is found to have a larger uncertainty in undersaturated conditions, whereas it becomes relatively certain in oversaturated conditions. All the analyses have special practical implications for enriched knowledge of stochastic arrival and departure processes at signals.

Forecasting of short-term traffic states on expressways by adopting spatial–temporal models has gained increasing attention. Traffic data from neighboring sites were demonstrated to provide valuable information for predicting traffic states at sites of interest. However, when one considers the need to analyze the multivariate nature of traffic states over spatial dimensions, as well as of different models for various times of day, the interaction effects between spatial–temporal patterns require further investigation. This study addressed this issue on a segment of Shanghai North–South Expressway. Temporal characteristics of traffic volumes and speeds were analyzed by dividing the time of day into ordinal periods with relatively stable states. Then, spatial vector autoregressive (VAR) models were constructed at typical analysis periods for volume and speed forecasting by considering different combinations of upstream and downstream impacts. The results showed that the impact of downstream traffic conditions on upstream traffic cannot be neglected, especially in peak periods. For off-peak periods, traffic states at a location largely depended on upstream states, while downstream states appeared to have fewer effects. In such cases, models incorporating only upstream states were proved able to achieve sufficient accuracy. In addition, encouraging forecasting results were found when VAR models were compared with traditional methods (e.g., autoregressive integrated moving average and historical average), which failed to consider the spatial component of spatial–temporal patterns. All analyses helped to demonstrate the applicability of VAR models and to provide practical guidance for incorporating spatial–temporal dynamics into forecasting of expressway traffic states.