Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation

Understanding the cause of cost overruns in transportation infrastructure projects has been a topic that has received considerable attention from academics and the popular press. Despite studies providing the essential building blocks and frameworks for cost overrun mitigation and containment, the problem still remains a pervasive issue for Governments worldwide. The interdependency that exists between ‘causes’ that lead to cost overruns materialising have largely been ignored when considering the likelihood and impact of their occurrence. The vast majority of the cost overrun literature has tended to adopt a deterministic approach in examining the occurrence of the phenomenon; in this paper a shift towards the adoption of pluralistic probabilistic approach to cost overrun causation is proposed. The establishment of probabilistic theory incorporates the ability to consider the interdependencies of causes so to provide Governments with a holistic understanding of the uncertainties and risks that may derail the delivery and increase the cost of transportation infrastructure projects. This will further assist in the design of effective mitigation and containment strategies that will ensure future transportation infrastructure projects meet their expected costs as well as the need of taxpayers.     

对道路拥挤收费经济学原理及其寻租现象的思考

道路拥挤收费是交通需求管理的一项重要措施,可以在一定程度上限制过大的交通量,但是,同时在我国这样的交通管理体制下,也存在一定的弊端.本文从经济学的原理出发,分析了道路拥挤收费的定价原则,拥挤收费可能出现寻租设租行为的原因,最后,提出使道路资源处在社会效益要求满意状态的相关建议和措施.     

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在社会剩余最大化情况下，城市道路网络中各个路段的拥挤费用等于相应的社会边际费用，道路拥挤收费可以抵消交通拥挤产生的外部不经济性，拥挤费用等于社会边际费用与用户边际费用的差额。通过拥挤收费使得社会剩余达到最大，同时导致用户剩余的损失，并衍生社会公平性问题。     

基于离散时间的定价与舱位控制联合决策

为使航空公司收益最大化, 将旅客订票与退票看作2个独立的随机过程, 利用泊松分布模拟整个预售期内旅客订票过程, 运用负指数分布模拟旅客退票过程, 从运用价格控制需求的角度出发建立了基于离散时间的航空机票定价与舱位控制联合决策模型, 采用动态规划的求解思想和方法对模型进行了求解, 确定在合适的时间段以何种价格销售机票, 设置每个时间段机票销售的数量限制, 通过一个算例对模型进行了验证。分析结果表明: 2~9折与全价机票应分别提前60.00、56.58、52.87、48.83、44.38、39.44、33.88、27.51、20.09d开始销售, 订票限制应分别为3、43、79、111、141、171、199、227、290。联合决策模型具有有效性和实用性, 可作为航空公司进行合理定价和舱位控制的参考。     

航空货运公司动态博弈下的即期舱位差别定价决策

为研究同一航线上2家航空货运公司舱位的差别定价决策问题, 建立了动态博弈定价模型, 确定2家公司在各销售阶段的舱位定价; 以大连-广州航段上大连机场货运公司和中国南方航空公司为例, 分析了2家公司在即期市场的舱位定价和收益情况。分析结果表明: 当2家公司采用差别定价模式时, 第1~4、5~6、7~10、11阶段的定价分别为9.7、12.6、13.6、15.2元·kg^-1, 当2家公司采用单一定价模式时, 各阶段的定价均为12.1元·kg^-1, 即在动态博弈定价下, 无论采用差别定价模式或是单一定价模式, 2家公司各销售阶段的舱位定价完全相同; 当2家公司分别采用差别定价模式与单一定价模式时, 所有阶段的总收益分别为50 928和49 519元, 说明在即期市场上采用差别定价模式销售舱位比采用单一定价模式能获得更大的收益; 当订舱需求受自身定价与对手定价的影响程度的比值分别为1.5、2.0、2.5时, 2家公司在各阶段的舱位定价均逐渐降低, 说明订舱需求受双方定价的影响程度越接近, 双方舱位定价的提升空间越大, 收益也越大。     

A forecast air pollution model applied to a hypothetical urban road pricing scheme: An empirical study in Madrid

Reducing the air pollution from increases in traffic congestion in large cities and their surroundings is an important problem that requires changes in travel behavior. Road pricing is an effective tool for reducing air pollution, as reflected currently urban road pricing outcomes (Singapore, London, Stockholm and Milan). A survey was conducted based on establishing a hypothetical urban road pricing system in Madrid (a random sample size n = 1298). We developed a forecast air pollution model with time series analysis to evaluate the consequences of possible air pollution decreases in Madrid. Results reveal that the hypothetical road pricing for Madrid could have highly significant effects on decreasing air pollution outside of the city and in the inner city during the peak operating time periods of maximum congestion (morning peak hours from 7:00 to 10:00 and evening peak hours from 18:00 to 20:00). Furthermore, this system could have significant positive effects on a shift toward using public transport and non-motorized modes inside the hypothetical toll zone. This reveals that the system has a high capacity to motivate a decrease in air pollution and impose more sustainable behavior for public transport users.     

Geometric matching and spatial pricing in ride-sourcing markets

This paper develops a model to investigate the effects of spatial pricing on ride-sourcing markets. The model is built upon a discrete time geometric matching framework that matches customers with drivers nearby. We demonstrate that a customer may be matched to a distant vehicle when demand surges, yielding an inefficient supply state. We further investigate market equilibrium under spatial pricing assuming a revenue maximizing platform, and find that the platform may resort to relatively higher price to avoid the inefficient supply state if spatial price differentiation is not allowed. Although spatial pricing facilitates market clearing, the platform may still set price more than the efficient level, which compromises the public interest. We then propose a commission rate cap regulation that reaps the flexibility of spatial pricing and can achieve the second best under some homogeneity assumptions.     

Dynamic pricing for managed lanes with multiple entrances and exits

Priced managed lanes are increasingly being used to better utilize the existing capacity of the roadway to relieve congestion and offer reliable travel time to road users. In this paper, we investigate the optimization problem for pricing managed lanes with multiple entrances and exits which seeks to maximize the revenue and minimize the total system travel time (TSTT) over a finite horizon. We propose a lane choice model where travelers make online decisions at each diverge point considering all routes on a managed lane network. We formulate the problem as a deterministic Markov decision process and solve it using the value function approximation (VFA) method for different initializations. We compare the performance of the toll policies predicted by the VFA method against the myopic revenue policy which maximizes the revenue only at the current timestep and two heuristic policies based on the measured densities on the managed and general purpose lanes (GPLs). We test the results on four different test networks. The primary findings from our research suggest the usefulness of the VFA method for determining dynamic tolls. The best-found objective value from the method at its termination is better than other heuristics for all test networks with average improvements in the objective ranging between 10% and 90% for revenue maximization and 0–27% for TSTT minimization. Certain VFA initializations obtain best-found toll profiles within first 5–50 iterations which warrants computational time savings. Our findings also indicate that the revenue-maximizing optimal policies follow the “jam-and-harvest” behavior where the GPLs are pushed towards congestion in the earlier time steps to generate higher revenue in the later time steps, a characteristic not observed for the policies minimizing TSTT.     

Dynamic operations and pricing of electric unmanned aerial vehicle systems and power networks

The emergence of electric unmanned aerial vehicle (E-UAV) technologies, albeit somewhat futuristic, is anticipated to pose similar challenges to the system operation as those of electric vehicles (EVs). Notably, the charging of EVs en-route at charging stations has been recognized as a significant type of flexible load for power systems, which often imposes non-negligible impacts on the power system operator’s decisions on electricity prices. Meanwhile, the charging cost based on charging time and price is part of the trip cost for the users, which can affect the spatio-temporal assignment of E-UAV traffic to charging stations. This paper aims at investigating joint operations of coupled power and electric aviation transportation systems that are associated with en-route charging of E-UAVs in a centrally controlled and yet dynamic setting, i.e., with time-varying travel demand and power system base load. Dynamic E-UAV charging assignment is used as a tool to smooth the power system load. A joint pricing scheme is proposed and a cost minimization problem is formulated to achieve system optimality for such coupled systems. Numerical experiments are performed to test the proposed pricing scheme and demonstrate the benefits of the framework for joint operations.     

How to road price in a world with electric vehicles and government budget constraints

In this paper we examine what characterizes second-best road prices targeting external costs from driving electric (EV) and conventional (ICEV) vehicles when there are distortionary labor taxes and binding government budget constraints. Further, we examine how this second-best pricing fits with government set goals of reducing CO₂ emissions. The paper further develops an analytical framework for assessing first- and second-best road prices on vehicle kilometers, extending it to include EVs and externalities that vary geographically and by time of day. We find that optimal road prices largely vary with external cost, but are also significantly affected by the interactions with the rest of the fiscal system. Not surprisingly, the highest road prices should be for ICEVs in large cities during peak hours due to high external costs. More surprisingly, we find that the road price for ICEVs in rural areas should be lower than that for EVs due to large fiscal interaction effects. These road prices give large welfare gains, but they lead to no reduction in carbon emissions when applying the currently recommended social cost of carbon.