A stochastic optimization model for transit network timetable design to mitigate the randomness of traveling time by adding slack time

Transit network timetabling aims at determining the departure time of each trip of all lines in order to facilitate passengers transferring either to or from a bus. In this paper, we consider a bus timetabling problem with stochastic travel times (BTP-STT). Slack time is added into timetable to mitigate the randomness in bus travel times. We then develop a stochastic integer programming model for the BTP-STT to minimize the total waiting time cost for three types of passengers (i.e., transferring passengers, boarding passengers and through passengers). The mathematical properties of the model are characterized. Due to its computational complexity, a genetic algorithm with local search (GALS) is designed to solve our proposed model (OPM). The numerical results based on a small bus network show that the timetable obtained from OPM reduces the total waiting time cost by an average of 9.5%, when it is tested in different scenarios. OPM is relatively effective if the ratio of the number of through passengers to the number of transferring passengers is not larger than a threshold (e.g., 10 in our case). In addition, we test different scale instances randomly generated in a practical setting to further verify the effectiveness of OPM and GALS. We also find that adding slack time into timetable greatly benefits transferring passengers by reducing the rate of transferring failure.     

Quantifying the relative contribution of factors to household vehicle miles of travel

Household vehicle miles of travel (VMT) has been exhibiting a steady growth in post-recession years in the United States and has reached record levels in 2017. With transportation accounting for 27 percent of greenhouse gas emissions, planning professionals are increasingly seeking ways to curb vehicular travel to advance sustainable, vibrant, and healthy communities. Although there is considerable understanding of the various factors that influence household vehicular travel, there is little knowledge of their relative contribution to explaining variance in household VMT. This paper presents a holistic analysis to identify the relative contribution of socio-economic and demographic characteristics, built environment attributes, residential self-selection effects, and social and spatial dependency effects in explaining household VMT production. The modeling framework employs a simultaneous equations model of residential location (density) choice and household VMT generation. The analysis is performed using household travel survey data from the New York metropolitan region. Model results showed insignificant spatial dependency effects, with socio-demographic variables explaining 33 percent, density (as a key measure of built environment attributes) explaining 12 percent, and self-selection effects explaining 11 percent of the total variance in the logarithm of household VMT. The remaining 44 percent remains unexplained and attributable to omitted variables and unobserved idiosyncratic factors, calling for further research in this domain to better understand the relative contribution of various drivers of household VMT.     

Anticipating emission-sensitive traffic management strategies for dynamic delivery routing

Traffic pollution is an increasing challenge for cities. Emissions such as nitrogen dioxides pose a major health threat to the city’s inhabitants. These emissions often accumulate to critical levels in local areas of the city. To react to these critical emission levels, cities start implementing dynamic traffic management systems (TMS). These systems dynamically redirect traffic flows away from critical areas. These measures impact the travel speeds within the city. This is of particular importance for parcel delivery companies. These companies deliver goods to customers in the city. To avoid long delivery times and higher costs, companies already adapt their routing with respect to changing traffic conditions. Still, a communication with the TMS may allow anticipatory planning to avoid potentially critical areas in the city. In this paper, we show how communication between TMS and delivery companies results in benefits for both parties. To exploit the provided information, we develop a dynamic routing policy anticipating potential future measures of the TMS. We analyze our algorithm in a comprehensive case study for the TMS of the city of Braunschweig, Germany, a city often used as reference for a typical European city layout. We show that for the delivery company, integrating the TMS’ information in their routing algorithms reduces the driving times significantly. For the TMS, providing the information results in less traffic in the polluted areas.     

The impact of extreme weather conditions on long distance travel behaviour

This paper examines traveller attitudes and responses towards disruption from weather and natural events. An internet-based travel behaviour survey was conducted with more than 2000 respondents in London and Glasgow. Of these respondents, 740 reported information on over 1000 long distance trips affected by extreme weather and natural events over the previous three years. Results show respondents are generally cautious towards travelling during extreme weather events. For a slight majority in the case of air and public transport, and a greater one in the case of car, travellers did not considerably alter their travel plan following the disruption. This was explained not only by less disruptive weather conditions (with heavy snow and volcanic ash being the most disruptive) and impact, but also by the relative importance of their trips. Differences between transport modes were not substantial. Business trips sometimes appeared to give travellers more flexibility, some other times not. Origin and destination did have an impact on reaction, as well as the presence of children whilst travelling. Mixed results were obtained about socio-economic and attitudinal variables. Age in particular did not appear to have a significant effect. Whilst most respondents did acknowledge no external influence in their decision, results showed an important contribution of transport organisation staff, as well as home and mobile internet technology. A limited but still considerable number of respondents indicated their closest friends/relatives as the main influence of their decisions. The results will help planners deploy strategies to mitigate the negative effects of weather related disruptions.     

有轨电车旅行速度提升方法

有轨电车的平均旅行速度不高。影响有轨电车旅行速度的关键因素为平交路口不停车通过率。从车站布局、平交路口设计、优先策略、时刻表匹配等视角分析了如何提升平交路口不停车通过率,提出了合理的车站布局、平交路口的优化设计、灵活配置路口优先策略及增加控制中心的协同等方法。     

Why do urban travelers select multimodal travel options: A repertory grid analysis

The increasing number of travelers in urban areas has led to new opportunities for local government and private mobility providers to offer new travel modes besides and in addition to traditional ones. Multimodal travel provides an especially promising opportunity. However, until now the underlying reasons why consumers choose specific alternatives have not been fully understood. Hence, the design of new travel modes is mainly driven by obvious criteria such as environmental friendliness and convenience but might not consider consumers’ real or latent needs. To close this research gap, sixty in-depth interviews with urban travelers were conducted. To identify the perceptual differences of customers among different travel modes, the repertory grid technique as an innovative, structured interview method was applied. Our data show that urban travelers distinguish and select travel alternatives based on 28 perceptual determinants. While some determinants associated with private cars such as privacy, flexibility and autonomy are key indicators of travel mode choice, costs and time efficiency also play a major role. Furthermore, by comparing travel modes to an ideal category, we reveal that some perceptual determinants do not need to be maximized in order to fulfill customer needs optimally. A comparison of consumers’ perceptual assessments of alternative travel modes identifies specific advantages and disadvantages of all alternatives, and provides fruitful implications for government and private mobility providers.     

基于出行时间预算的SUE交通分配

基于均匀分布的路段容量,分析了降级路网中路段和路径出行时间的随机变动,假定出行者根据以往的出行经验获取路径出行时间的可变性,并以出行时间预算的形式将这种可变性纳入到其路径选择过程中,进而定义路径出行时间预算为路径出行时间均值与出行时间安全边际之和.在此基础上,采用变分不等式技术构建了基于出行时间预算的多用户类型弹性需求随机用户均衡交通分配模型,并证明了模型解的等价性.     

基于神经网络的应急车辆行程时间计算模型

消防、救护等应急车辆的行程时间直接影响救援的质量.在分析影响应急车辆行程时间的因素基础上,选取了行驶距离和出发时刻两个影响因素,应用BP神经网络模型,建立了应急车辆行程时间计算模型,求取了影响因素对行程时间的影响重要度因子.为应急车辆的选址和调度提供了依据.     

ATIS影响下的混合随机用户均衡交通分配模型研究

在考虑信息对出行者路径选择行为影响的基础上,基于路网混合随机用户均衡建模理论,建立了ATIS影响下的混合随机用户均衡交通分配模型,证明了该数学规划模型解的等价性,设计了求解算法,并利用算例进行了计算分析.     

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路段行程时间的长短影响出行者的效率.本文从停车巡游角度给出了停车诱导对出行者出行时间的影响模型.对运行中车辆的密度函数增减关系进行分析，给出停车诱导信息的路段行驶时间函数解析关系式，推导出停车诱导信息对出行时间影响关系.进一步通过西单地区停车诱导系统调查结果分析说明驾车出行者对停车诱导信息的需求程度，探讨停车诱导信息系统用于解决交通出行时间效率问题。