Alpha-reliable combined mean traffic equilibrium model with stochastic travel times

Based on the reliability budget and percentile travel time(PTT) concept, a new travel time index named combined mean travel time(CMTT) under stochastic traffic network was proposed. CMTT here was defined as the convex combination of the conditional expectations of PTT-below and PTT-excess travel times. The former was designed as a risk-optimistic travel time index, and the latter was a risk-pessimistic one. Hence, CMTT was able to describe various routing risk-attitudes. The central idea of CMTT was comprehensively illustrated and the difference among the existing travel time indices was analyzed. The Wardropian combined mean traffic equilibrium(CMTE) model was formulated as a variational inequality and solved via an alternating direction algorithm nesting extra-gradient projection process. Some mathematical properties of CMTT and CMTE model were rigorously proved. Finally, a numerical example was performed to characterize the CMTE network. It is founded that that risk-pessimism is of more benefit to a modest(or low) congestion and risk network, however, it changes to be risk-optimism for a high congestion and risk network.

Individual departure time decision considering departure scheduling utility

The scheduling utility plays a fundamental role in addressing the commuting travel behaviours. A new scheduling utility,termed as DMRD-SU, was suggested based on some recent research findings in behavioural economics. DMRD-SU admitted the existence of positive arrival-caused utility. In addition, besides the travel-time-caused utility and arrival-caused utility, DMRD-SU firstly took the departure utility into account. The necessity of the departure utility in trip scheduling was analyzed comprehensively,and the corresponding individual trip scheduling model was presented. Based on a simple network, an analytical example was executed to characterize DMRD-SU. It can be found from the analytical example that: 1) DMRD-SU can predict the accumulation departure behaviors at NDT, which explains the formation of daily serious short-peak-hours in reality, while MRD-SU cannot; 2)Compared with MRD-SU, DMRD-SU predicts that people tend to depart later and its gross utility also decreases faster. Therefore,the departure utility should be considered to describe the traveler's scheduling behaviors better.

基于AFC数据的地铁乘客候车时间估计方法

考虑因拥挤引发的留乘现象对候车时间的影响,本文依据自动售检票数据(Automatic Fare Collection,AFC)和列车运行图的关系,提出基于乘客出行行程的候车时间估计方法,以估计进站及换乘候车时间。分析了走行时间、留乘次数、乘坐列车等与候车时间关系,以无换乘乘客和有换乘乘客数据分别建立了进站候车时间和换乘候车时间模型;利用现有研究成果估计了进出站走行分布和留乘概率;通过构造换乘乘客行程的极大似然函数估计换乘走行方法。以成都地铁作为案例分析求解,结果表明:仿真旅行时间与真实旅行时间对比,验证了模型建立的正确性,否定了“乘客候车时间为列车发车间隔时间一半”的假设。