Insights on disruptions as opportunities for transport policy change

Policy change is characterised as being slow and incremental over long time periods. In discussing a radical shift to a low carbon economy, many researchers identify a need for a more significant and rapid change to transport policy and travel patterns. However, it is not clear what is meant by rapid policy change and what conditions might be needed to support its delivery.Our contention in this paper is that notions of habit and stability dominate thinking about transport trends and the policy responses to them. We limit variability in our data collection and seek to design policies and transport systems that broadly support the continuation of existing practices. This framing of the policy context limits the scale of change deemed plausible and the scope of activities and actions that could be used to effect it.This paper identifies evidence from two sources to support the contention that more radical policy change is possible. First, there is a substantial and on-going churn in household travel behaviour which, harnessed properly over the medium term, could provide the raw material for steering behaviour change. Secondly, there is a growing evidence base analysing significant events at local, regional and national level which highlight how travellers can adapt to major change to network conditions, service availability and social norms. Taken together, we contend that the population is far more adaptable to major change than the policy process currently assumes.Disruptions and the responses to them provide a window on the range of adaptations that are possible (and, given that we can actually observe people carrying them out, could be more widely acceptable) given the current configuration of the transport system. In other words, if we conceptualise the system as one in which disruptions are commonplace, then different policy choices become tractable. Policy change itself can also be seen as a positive disruption, which could open up a raft of new opportunities to align policy implementation with the capacity for change. However, when set against the current framing of stability and habit, disruption can also be a major political embarrassment. We conclude that rather than being inherently problematic, disruption are in fact an opportunity through which to construct a different approach to transport policy that might enable rather than frustrate significant, low carbon change.     

城市交通系统的公共政策研究

城市交通系统是一个复杂的动态系统,由它产生的城市交通问题具有普遍性、紧迫性和长期性。因此,要解决城市交通问题既要参考国内外经验,又要立足实际。只有循序渐进地不断修正城市交通政策,才能切实提高政府的执政能力,建立一个适应城市经济发展和满足市民实际需求的城市交通系统。     

西部陆海新通道海铁联运发展与对策研究

西路陆海新通道作为西部地区的重要运输通道,内连丝绸之路经济带和长江经济带,外接东南亚、西亚和欧洲地区,具有重要的战略地位。在阐述西部陆海新通道及其海铁联运现状的基础上,针对目前西部陆海新通道合作共建机制不够完善、基础设施有待改进、双向货源不均衡等问题,从完善合作共建机制、加快海铁联运基础设施建设进度和推动双向货源均衡发展等方面对西部陆海新通道多式联运发展对策进行分析,以提升西部陆海新通道的运输质量,有效吸引西部陆海新通道沿线地区的贸易往来,实现西部陆海新通道的协同健康发展。     

西安局集团公司货物运输结构优化对策探讨

铁路是国民经济大动脉,铁路企业货物运输结构与区域经济结构紧密相关。从货物运输品类结构煤炭发送量占比较高、到发结构货物到发比严重失衡和增长速度等方面,阐述西安局集团公司货物运输结构现状,从重发送轻到达、重大宗轻零散、大宗货物两端运输衔接顺畅和零散货物运输时效难以保证等方面,分析货物运输结构问题的影响因素。在此基础上,提出西安局集团公司货物运输结构优化对策,即:以完善考核和清算办法引导货物运输结构优化,以抓好重点品类运输促进非煤货物上量,以综合经营理念释放物流基地效能,不断完善西安局集团公司货物运输结构,促进货物运输高质量发展。     

北京局集团公司货运高质量发展对策研究

北京局集团公司以"公转铁"为主攻方向,深入推进铁路货运增量行动,提高货运产品供给质量,提升货运市场竞争力,实现货运稳定增长,引领铁路货运高质量发展。通过阐述北京局集团公司货运发展现状,从货运增量任务艰巨、物流市场竞争激烈、货运站场能力亟需补强、信息系统缺乏有效整合等方面分析影响因素,探讨北京局集团公司货运高质量发展对策,即:多措并举,实现货运稳定增长;提质增效,提升货运产品供给质量;创新驱动,引领货运高质量发展。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

How the cap limit for food-crop-based biofuels may affect France’s stakeholders by 2030? A range-based multi-actor multi-criteria analysis

From 2012 to 2016, the long-term signal in the biofuel market changed almost once per year, leading to a drastic decrease in investments and contributing to multiple production unit closures in France and around Europe. The European Commission proposed a new renewable energy directive that includes a 3.8% cap limit on the contribution of food-crop biofuels by 2030. Given the role of biofuels in green growth, the bioeconomy, and renewable energy incorporation targets, how will this measure affect the stakeholders by 2030? Will it lead to, contribute to, or hamper their sustainability criteria? This paper aims to contribute to this debate by studying the case of France. To this end, our methodology—the range-based multi-actor multi-criteria analysis—aims to (1) explicitly consider the stakeholder groups and their sustainability criteria; (2) evaluate and compare how the cap limit will affect these sustainability criteria whether or not advanced biofuels are deployed by 2030; (3) capture the uncertainty of the context evolution and biofuel capacity to fulfil the stakeholders’ sustainability criteria by means of a Monte Carlo. The results suggest that the cap limit is a double-edged sword for the stakeholders and their sustainability criteria. Shifting towards advanced biofuels while limiting the food-crop biofuels is the better alternative for most of the stakeholders. Nevertheless, given biofuel policy instability and the lost confidence of investors, such a shift may not occur by 2030. In such a case, this paper demonstrates that the cap limit may highly and negatively affect the stakeholders and their sustainability targets, whereas fostering French food-crop biofuel production at its full capacity level constitutes a better alternative. As no alternative is suited to all actors simultaneously, this paper also studies the strengths and weaknesses of these alternatives from each stakeholder groups’ perspective.     

Stability of transportation networks under adaptive routing policies

Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic Equilibria” (MTE), in which users update their route choice at each intersection of the road network based on traffic conditions, to the case of non-equilibrium conditions, while preserving consistency with known existence and uniqueness results on MTE. We then exhibit sufficient conditions on the network topology and the latency functions for those MTEs to be stable in the sense of Lyapunov for a single destination problem. For various more restricted classes of network topologies motivated by the observed properties of travel patterns in the Singapore network, under certain assumptions we prove local exponential stability of the MTE, and derive analytical results on the sensitivity of the characteristic time of convergence to network and traffic parameters. The results proposed in this work are illustrated and validated on synthetic toy problems as well as on the Singapore road network with real demand and traffic data.     

Factors influencing bike share membership: An analysis of Melbourne and Brisbane

The number of bike share programs has increased rapidly in recent years and there are currently over 700 programs in operation globally. Australia’s two bike share programs have been in operation since 2010 and have significantly lower usage rates compared to Europe, North America and China. This study sets out to understand and quantify the factors influencing bike share membership in Australia’s two bike share programs located in Melbourne and Brisbane. An online survey was administered to members of both programs as well as a group with no known association with bike share. A logistic regression model revealed several significant predictors of membership including reactions to mandatory helmet legislation, riding activity over the previous month, and the degree to which convenience motivated private bike riding. In addition, respondents aged 18–34 and having docking station within 250 m of their workplace were found to be statistically significant predictors of bike share membership. Finally, those with relatively high incomes increased the odds of membership. These results provide insight as to the relative influence of various factors impacting on bike share membership in Australia. The findings may assist bike share operators to maximize membership potential and help achieve the primary goal of bike share – to increase the sustainability of the transport system.     

A new composite decision support framework for strategic and sustainable transport appraisals

This paper concerns the development of a new decision support framework for the appraisal of transport infrastructure projects. In such appraisals there will often be a need for including both conventional transport impacts as well as criteria of a more strategic and/or sustainable character. The proposed framework is based on the use of cost-benefit analysis featuring feasibility risk assessment in combination with multi-criteria decision analysis and is supported by the concept of decision conferencing. The framework is applied for a transport related case study dealing with the complex decision problem of determining the most attractive alternative for a new fixed link between Denmark and Sweden – the so-called HH-connection. Applying the framework to the case study made it possible to address the decision problem from an economic, a strategic, and a sustainable point of view simultaneously. The outcome of the case study demonstrates the decision making framework as a valuable decision support system (DSS), and it is concluded that appraisals of transport projects can be effectively supported by the use of the DSS. Finally, perspectives of the future modelling work are given.