Shoufeng Ma

A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO2 emissions: A case study of Beijing

Abstract With the accelerating process of urbanization, developing countries are facing growing pressure to pursue energy savings and emission reductions, especially in urban passenger transport. In this paper, we built a Beijing urban passenger transport carbon model, including an economy subsystem, population subsystem, transport subsystem, and energy consumption and CO2 emissions subsystem using System Dynamics. Furthermore, we constructed a variety of policy scenarios based on management experience in Beijing. The analysis showed that priority to the development of public transport (PDPT) could significantly increase the proportion of public transport locally and would be helpful in pursuing energy savings and emission reductions as well. Travel demand management (TDM) had a distinctive effect on energy savings and emission reductions in the short term, while technical progress (TP) was more conducive to realizing emission reduction targets. Administrative rules and regulations management (ARM) had the best overall effect of the individual policies on both energy savings and emission reductions. However, the effect of comprehensive policy (CP) was better than any of the individual policies pursued separately. Furthermore, the optimal implementation sequence of each individual policy in CP was TP→PDPT→TDM→ARM.

A Day-to-Day Route Choice Model Based on Reinforcement Learning

Day-to-day traffic dynamics are generated by individual traveler’s route choice and route adjustment behaviors, which are appropriate to be researched by using agent-based model and learning theory. In this paper, we propose a day-to-day route choice model based on reinforcement learning and multiagent simulation. Travelers’ memory, learning rate, and experience cognition are taken into account. Then the model is verified and analyzed. Results show that the network flow can converge to user equilibrium (UE) if travelers can remember all the travel time they have experienced, but which is not necessarily the case under limited memory; learning rate can strengthen the flow fluctuation, but memory leads to the contrary side; moreover, high learning rate results in the cyclical oscillation during the process of flow evolution. Finally, both the scenarios of link capacity degradation and random link capacity are used to illustrate the model’s applications. Analyses and applications of our model demonstrate the model is reasonable and useful for studying the day-to-day traffic dynamics.

Power laws in the cluster sizes of group-crossing pedestrians: empirical evidence and modelling of causes

Pedestrian-vehicle mixed traffic at unsignalized crosswalks is a self-organizing system, because of the absence of external control. Consequently, pedestrian group-crossing behaviour may be expected to exhibit the power-law phenomenon, which is often an indicator of self-organized criticality. The assumption that the distribution of cluster sizes may obey power laws was theoretically supported by Xin et al. To test this claim empirically, we observed pedestrian collective motion under natural conditions, using video recordings. These real data confirmed the existence of the power-law phenomenon. A simulation study was then undertaken to uncover the mechanism that generated the observations. The observed patterns could be fully replicated if the behaviour of pedestrians and motorists were modelled, in particular the yielding behaviour of motorists, when approaching crossings and the adaptive behaviour of pedestrians as they increased in number. As well as providing empirical evidence, our findings also provide a deeper understanding of the patterns of the pedestrian collective motion. The results may have implications in the validation of mixed traffic simulation models.

Empirical and simulation study of traffic delay at un-signalized crosswalks due to conflicts between pedestrians and vehicles

ABSTRACT The average is usually applied to describe the traffic delay, and the environmental factors are considered to influence it. However, the distribution and the influencing individual behavior on traffic delay is seldom investigated. An observational study shows that pedestrian and vehicle delay follow exponential and power-law distributions. Since they are left-skewed and fat-tailed, the average delay fails to describe the Level Of Service (LOS) in some traffic conditions. To confirm that traffic delay follows these distributions in other situations, a simulation study is conducted. The results coincide with the empirical ones. A new indicator—the average of the longest 20% delay, is developed. It provides a better estimate of LOS than the average of the whole in describing traffic conditions, especially the congested ones. Moreover, pedestrian assertive behavior is a critical influencing factor on traffic delay. The results indicate that the characteristics of the delay distribution is worthy of note. Highlights Distribution of pedestrian and vehicle delay is left-skewed and fat-tailed. Traffic delay distributions explored through an observational and a simulation study. Pedestrian assertiveness is considered in the simulation model. A new indicator of LOS estimation is developed based on the 80/20 rule.

A cellular automaton model reproducing realistic propagation speed of downstream front of the moving synchronized pattern

ABSTRACT The moving synchronized pattern (MSP) is an important traffic pattern that can emerge when traffic breakdown occurs. However, up to now most models cannot reproduce a realistic propagation speed of downstream front of the MSP, which significantly weakens their applications in traffic breakdown prediction and control. In this paper, a new brake light cellular automaton model is proposed, which assumes that: (i) the drivers would be sensitive to the brake light only when their speeds are larger than a critical speed; (ii) the anticipated deceleration of a preceding vehicle increases with the increase of the speed of the following vehicle. Simulation analysis shows that the new model can depict traffic breakdown and related synchronized traffic flow patterns. Importantly, it can realistically reproduce the propagation speed of downstream front of the MSP. Finally, the new model is calibrated and validated by NGSIM detector data.