Optimal locations and travel time display for variable message signs

This paper first develops a network equilibrium model with the travel time information displayed via variable message signs (VMS). Specifically, the equilibrium considers the impact of the displayed travel time information on travelers’ route choices under the recurrent congestion, with the endogenous utilization rates of displayed information by travelers. The existence of the equilibrium is proved and an iterative solution procedure is provided. Then, we conduct the sensitivity analyses of the network equilibrium and further propose a paradox, i.e., providing travel time information via VMS to travelers may degrade the network performance under some poor designs. Therefore, we investigate the problem of designing the VMS locations and travel time display within a given budget, and formulate it as a mixed integer nonlinear program, solved by an active-set algorithm. Lastly, numerical examples are presented to offer insights on the equilibrium results and optimal designs of VMS.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

A comparative life-cycle energy and emissions analysis for intercity passenger transportation in the U.S. by aviation,intercity bus,and automobile

Intercity passenger trips constitute a significant source of energy consumption, greenhouse gas emissions, and criteria pollutant emissions. The most commonly used city-to-city modes in the United States include aircraft, intercity bus, and automobile. This study applies state-of-the-practice models to assess life-cycle fuel consumption and pollutant emissions for intercity trips via aircraft, intercity bus, and automobile. The analyses compare the fuel and emissions impacts of different travel mode scenarios for intercity trips ranging from 200 to 1600 km. Because these modes operate differently with respect to engine technology, fuel type, and vehicle capacity, the modeling techniques and modeling boundaries vary significantly across modes. For aviation systems, much of the energy and emissions are associated with auxiliary equipment activities, infrastructure power supply, and terminal activities, in addition to the vehicle operations between origin/destination. Furthermore, one should not ignore the embodied energy and initial emissions from the manufacturing of the vehicles, and the construction of airports, bus stations, highways and parking lots. Passenger loading factors and travel distances also significantly influence fuel and emissions results on a per-traveler basis. The results show intercity bus is generally the most fuel-efficient mode and produced the lowest per-passenger-trip emissions for the entire range of trip distances examined. Aviation is not a fuel-efficient mode for short trips (<500 km), primarily due to the large energy impacts associated with takeoff and landing, and to some extent from the emissions of ground support equipment associated with any trip distance. However, aviation is more energy efficient and produces less emissions per-passenger-trip than low-occupancy automobiles for trip distances longer than 700–800 km. This study will help inform policy makers and transportation system operators about how differently each intercity system perform across all activities, and provides a basis for future policies designed to encourage mode shifts by range of service. The estimation procedures used in this study can serve as a reference for future analyses of transportation scenarios.     

The potential mitigation of CO2 emissions via modal substitution of high-speed rail for short-haul air travel from a life cycle perspective – An Australian case study

The objective of this study is to provide a strategic evaluation of the mitigation of CO₂ emissions via modal substitution of high-speed rail for short-haul air travel on the Sydney–Melbourne, Australia city-pair from a life cycle perspective. It has been demonstrated that when considering CO₂ emissions from vehicle operations, the modal shift from air to high-speed rail on this city-pair has the potential to provide a means of CO₂ mitigation. However, uncertainty exists with regard to the level of mitigation potential when considering the whole-of-life performance of the systems. Given the significant difference in the infrastructure requirements between the air mode and the high-speed rail mode, this study quantifies the life cycle CO₂ load attributable to each system and examines the effect on CO₂ mitigation potential. The study concluded that while the inclusion of the linehaul infrastructure did increase the CO₂ load associated with high-speed rail mode, it did not equate to or exceed the CO₂ load per trip as experienced by the air mode. The avoided annual life cycle CO₂ emission in the target year 2056 was 0.37 Mt representing an 18% reduction when compared to the air mode only on the city pair. In fact, the scenario comparison indicated that the substitution of high-speed rail for short-haul air travel on the city pair resulted in CO₂ emissions avoidance throughout the longitudinal period.     

Adoption intentions for micro-mobility – Insights from electric scooter sharing in Taiwan

Micro-mobility may alleviate a number of challenges facing big cities today, and offer a pathway toward more sustainable urban transportation. This research investigates what factors influence university students’ intention to use an electric scooter sharing service. A theoretical framework adapting the theory of planned behaviour was used. Survey responses from 471 university students in Taiwan were collected. Data were analysed using factor analysis and structural equation modelling. Respondents with different levels of usage intention and in different stages of behavioural change display distinct reasoning patterns. Lack of perceived compatibility with personal values, mobility needs and life-style particularly drives students with low usage intention and pre-contemplators, who show signs of “green hypocrisy”. Awareness-knowledge about the sharing system and environmental values influence the formation of usage intention in indirect ways. This study is intended to be a reference for subsequent conceptual and empirical work on e-scooter sharing in particular, and shared mobility with other types of powered two-wheelers.     

考虑异质性的经典随机后悔最小化模型改进研究

传统基于后悔理论的交通行为模型对异质性考虑不充分，对真实选择行为的解释存在不足.本文利用韦伯比率，考虑出行者对各方式属性变量感知的异质性，对经典随机后悔最小化模型进行改进，分别建立基于随机效用最大化，经典随机后悔最小化和改进经典随机后悔最小化的选择模型；以网约车选择行为为例进行实证研究，验证改进模型效果.结果表明：3种模型参数标定结果具有一致性，改进模型的拟合优度(0.271)和命中率(75.8%)相较于另外两个模型均较优；改进模型能更好地描述多维属性决策过程中的半补偿原则和折中效应，可以提高模型对真实选择行为的解释能力.     

Discussing the “positive utilities” of autonomous vehicles: will travellers really use their time productively?

ABSTRACT

Autonomous vehicles (AVs) are expected to reshape travel behaviour and demand in part by enabling productive uses of travel time—a primary component of the “positive utility of travel” concept—thus reducing subjective values of travel time savings (VOT). Many studies from industry and academia have assumed significant increases in travel time use and reductions in VOT for AVs. In this position paper, I argue that AVs’ VOT impacts may be more modest than anticipated and derive from a different source. Vehicle designs and operations may limit activity engagement during travel, with AV users feeling more like car passengers than train riders. Furthermore, shared AVs may attenuate travel time use benefits, and productivity gains could be limited to long-distance trips. Although AV riders will likely have greater activity participation during travel, many in-vehicle activities today may be more about coping with commuting burdens than productively using travel time. Instead, VOT reductions may be more likely to arise from a different “positive utility”—subjective well-being improvements through reduced stresses of driving or the ability to relax and mentally transition. Given high uncertainty, further empirical research on the experiential, time use, and VOT impacts of AVs is needed.     

复杂环境下连续弯钢箱梁耐火性能提升方法

交通类火灾严重威胁钢结构桥梁的耐久性和安全性。为提升复杂环境(开放火灾和弯桥荷载)下连续弯钢箱梁的耐火性能,增强钢结构桥梁的安全服役寿命,选取大型立交桥枢纽工程中两跨连续弯钢箱梁为研究对象,通过建立耐火试验验证的钢箱梁与混凝土刚性基层协同工作的数值预测模型,深入揭示开放环境碳氢火灾下传热模式和结构特征耦合的箱梁力学行为演化规律。研究了局部环境火灾作用下结构的高温响应与失效模式,分析了复杂荷载状况、弯曲半径与支座布置方式对连续弯钢箱梁火灾响应行为的影响,提出了复杂环境下连续弯钢箱梁的耐火性能提升方法。研究结果表明:连续弯钢箱梁在火灾下的内外侧挠度差值不断增大,主梁内外侧支座反力的变化呈相反趋势,并且在受火初期支座反力变化程度剧烈;受火区域边缘靠近中支点的底板与腹板严重屈曲从而先形成塑性铰,然后在受火跨跨中形成塑性铰,随即整跨结构发生突然性垮塌;荷载水平的增大会显著缩短其耐火极限,受火前期及时撤离桥上的车辆荷载能够有效地延缓变形发展并且避免结构的突然性垮塌;曲率半径小于200 m会显著加剧连续弯钢箱梁高温下的弯扭耦合效应,增大主梁内外侧挠度差值与内外侧支座反力变化幅度,削弱火灾下结构的整体稳定性能;在钢结构桥梁抗火设计时中支点应设置抗扭支座,常温下支座的布置方式对火灾下连续弯钢箱梁的支座受力状况改善甚微,应在支座与梁端附近增设外部限位装置以防止结构变形过大。研究结论可为提升复杂环境下钢结构桥梁抵抗火灾的能力以及增强安全服役寿命提供设计依据。