Accurate predictions of bus service time are a prerequisite to improve the efficiency of bus transit. Field observations identified the degree of passenger crowding on the bus as one of the primary causes for the unpredictability of passengers' average boarding time (ABT). Consequently, the internal areas of buses were divided for the first time to assess the impact of the passenger density distribution (PDD) on the ABT, using field data. An effective BPR function was proposed that accurately predicts the passengers' ABT. Then, to show the influence of passenger movement preference and density distribution on ABT, a new floor field model was developed. Calibration and validation results indicate that the model can reproduce the dynamic moving process of passengers' distribution on the bus and clarify the influence of PDD on ABT. Finally, three bus design strategies are proposed to enhance boarding efficiency, and the simulation results show that the optimal strategy improves passenger ABT and improve the operational performance of bus transit. These findings provide theoretical support for bus design as well as for bus operation.

To further enhance the adaptability of traffic model in actual traffic flow, this paper puts forward a lattice model with considering both the predictive effect and the continuous density of historical information. The critical stability condition is derived from linear stability analysis, and the phase diagram clearly shows that considering the predictive effect and the continuous historical density information is beneficial to reduce traffic congestion. Then, a mKdV equation is obtained by nonlinear analysis, which enable to depict the development process of blocked flow. Finally, the numerical simulation results are confirmed that the predictive effects and continuous historical density information have the ability to suppress traffic congestion.

Connected vehicles that communicate via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies have become a hot topic in the transportation control field. Benefit from these new technologies, this work constructs a modified intelligent microscopic model considering driver reaction to future motion of preceding vehicles. First, we mimic the impact of anticipative motion of multiple preceding vehicles on the traffic stream, developing a new model, which is further investigated by applying the linear and nonlinear analytical methods. Also, the corresponding neutral and coexisting stability conditions are determined. Analytical results indicate that the information about the future motion of multiple preceding vehicles can improve traffic stability significantly. Besides, the phase diagram is presented by plotting the neutral stability lines and coexisting curves; three zones are created by partitioning the phase diagram with the neutral line and coexisting line, namely, the unsteady zone under the coexisting line, the stable zone above the neutral line, and the metastable zone between the two lines. Finally, the emulation experiments are conducted to validate the analytical conclusions. The results show that the information on a changing trend of multiple headways obviously smooths the traffic flow and restrains the formation of traffic jams. (C) 2020 Elsevier B.V. All rights reserved.

This paper aims to control the disturbed vehicle to move smoothly by a feedback signal in the optimal velocity model. Using the control signal of the variation rate of the optimal velocity, a new feedback control traffic flow model is proposed to explore the propagation mechanism of traffic flow oscillation. The linear stability condition is derived by using frequency-domain method, and the new control method is further verified by numerical simulation. Both the analytical analysis and numerical experiments show that this novel feedback mechanism can effectively suppress traffic congestion. (C) 2020 Elsevier B.V. All rights reserved.

This paper devises a new two-lane lattice hydrodynamic model (TLHM) to explore driver's predictive effect (DPE) on traffic oscillation. First, a linear approach is conducted to analytically predict the DPE on traffic performance. Theoretical analysis shows that with the help of DPE, the traffic flow stability will be gradually enhanced. Then, nonlinear analysis is implemented to explore the characteristics of traffic oscillation when sensitivity coefficient is near the critical point. The modified KdV equation derived from the new model and its analytical solution related kink-antikink density waves are obtained. Finally, numerical experiments show that the DPE can effectively dampen the growth of oscillation, which is well consistent with the theoretical analysis of the new model. (C) 2020 Published by Elsevier B.V.

Scholars from various countries have done a lot of research on predicting the dwell time of buses at stations, however, the passenger boarding time is hard to accurately predicted. Through field investigation, we found that passenger boarding time is not only influenced by external facilities, but also closely related to the number and distribution of passengers on the bus. Therefore, this article analyzes the passenger distribution law in the bus, and finds out the impact of passenger distribution in different areas of the bus on passenger boarding time through theoretical analysis. Secondly, we establish a cellular automata evolution model, which selects attraction and competitiveness as the driving force of passengers based on their motion characteristics. The simulation results prove that the model can exhibit the actual passenger distribution and movement preferences in the bus, and actually predict the boarding time of passengers in different scenarios.

The expressway traffic incidents have the characteristics of high harmful, strong destructive and refractory. Incident detection can guarantee smooth operation of the expressway, reduce traffic congestion and avoid secondary accident by informing the accident, detection and treatment timely. In this paper, an incident detection method is proposed using the toll station data that takes into account the traffic ratio at the entrances and crossway in the network. The expressway traffic simulation model is improved and a simulation algorithm is established to describe the movement of the vehicles. A numerical example is experimented on the expressway network of Shandong province. The proposed method can effectively detect the expressway incidents, and dynamically estimate the traffic network states so as to provide advice for the highway management department.