دانلود رایگان مقاله انگلیسی طراحی عوارض مبتنی بر سرعت برای باجه های عوارض مناطق پر تراکم به همراه ترجمه فارسی
| عنوان فارسی مقاله: | طراحی عوارض مبتنی بر سرعت برای باجه های عوارض مناطق پر تراکم |
| عنوان انگلیسی مقاله: | Speed-based toll design for cordon-based congestion pricing scheme |
| رشته های مرتبط: | مهندسی عمران و مهندسی کامپیوتر، برنامه ریزی حمل و نقل، مهندسی الگوریتم ها و محاسبات و هوش مصنوعی |
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| نشریه | الزویر – Elsevier |
| کد محصول | f419 |
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بخشی از ترجمه فارسی مقاله: 1. معرفی 1.1 مطالعات مربوطه |
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بخشی از مقاله انگلیسی: 1. Introduction The toll design problem for congestion pricing schemes refers to the determination of the optimal toll charge according to one or more objectives, based on given charging locations. Two congestion pricing schemes have received much attention and been comprehensively investigated: first-best (Pigouvian) and second-best pricing (see the monograph by Lewis, 1993; Yang and Huang, 2005; Lawphongpanich et al., 2006; Small and Verhoef, 2007; and a recent review by de Palma and Lindsey, 2011). Some system-wide objectives are usually adopted for the toll design of these two schemes, for instance, the total social benefit, total travel time and toll revenue. However, compared with these system-wide objectives, government and network authorities are usually more concerned about the traffic conditions in a central business district (CBD), the commercial heart of a city, where traffic congestion is likely to cause greater economic losses and worse impacts on a city’s image. Thus, regarding the practical implementation of congestion pricing schemes, mitigating traffic congestion in the CBD is usually taken as a primary target. A cordon-based congestion pricing scheme is advantageous for improving the traffic condition in a CBD as it defines a pricing cordon, encircling a certain area (usually the CBD), and charges each incoming vehicle; the total inbound volume is thus limited and traffic congestion in this area significantly ameliorated. Additionally, area-wide cordon-based pricing schemes are more convenient in terms of operation and supervision, compared with first-best and second-best pricing, which aims to optimize a system-wide objective. Until now, most applications of congestion pricing are cordon-based, for instance the Area Licensing Scheme (ALS) in Singapore (Phang and Toh, 1997; Li, 1999) that was upgraded in 1998 to the Electronic Road Pricing (ERP) system (Olszewski and Xie, 2005), the London Congestion Charging Scheme (Santos, 2008), and a more recent trial in Stockholm (Eliasson, 2009). It should be pointed out that the Stockholm congestion charge scheme also levies tolls on vehicles leaving the charging area. Average travel speed is an ideal measure of the traffic conditions in an area guarded by a pricing cordon (called a cordon area hereafter), in that it is much easier to observe than traffic column or density (Li, 2002) and is also a better representative of the commuter’s driving experience. In the cordon-based ERP system in Singapore, the objective is to keep the average speed of vehicles in the cordon area within a target range: [20, 30] km/h, which is achieved by adjusting the toll charges (Olszewski and Xie, 2005). Note that the lower-bound of this range guarantees a reasonably rapid travel. The upper-bound on travel speed is a concern about traffic safety, and it also avoids a waste of the road resources by ensuring that sufficient vehicles are traveling in the cordon area. Herein, the search for a toll charge pattern that will keep the average travel speed of vehicles in the cordon area within a predetermined target range is named the speed-based toll design problem. Despite its practical significance, the problem is still an open question, since few of the existing studies of toll design problems have taken the traffic conditions in the CBD as an objective or used travel speed as a criterion for network performance. Modeling the toll design problem requires an analysis of the commuters’ route choice problem, and a simple assumption is made here that the commuters will select the path with minimal travel cost based on their pre-trip perceived travel times. The probit-based stochastic user equilibrium (SUE) principle is adopted as a framework for the route choice problem, in view of its better suitability to realistic conditions compared with the deterministic user equilibrium (DUE) and logit-based SUE cases (Sheffi, 1985, p. 318). The commuters’ travel costs consist of two components: travel time cost and toll charge, which are expressed in different units. The value-of-time (VOT) is needed to convert the toll charges into time units for the analysis (e.g., Lam and Small, 2001; Yang et al., 2001; Small et al., 2005). The VOT is largely influenced by the commuters’ level of income and trip emergency, thus it can vary significantly among commuters. It is difficult to find two commuters on the network with identical VOT values, thus at an aggregate level, it is more suitable to define the VOT as a continuously distributed random variable. The probit-based SUE principle and continuously distributed VOT both increase the challenges involved in modeling and solving the speed-based toll design problem, which are the aims of this paper. 1.1. Relevant studies It has been well recognized that marginal cost pricing is a solution to the first-best pricing scheme with the objective of optimizing a system-wide index such as the total social benefit or total travel time (Yang and Huang, 2005; Lawphongpanich and Yin, 2012). The validity of marginal cost pricing for general transportation networks has been proven by many studies under different assumptions, for example, with elastic demand (Huang and Yang, 1996), with logit-based SUE constraints (Yang and Huang, 1998), with general SUE constraints (Maher et al., 2005), and with stochastic demand (Sumalee and Xu, 2011), to name a few. The optimal marginal cost pricing scheme can easily be obtained by solving a traffic assignment problem. An engineering-oriented trial-and-error method was proposed by Yang et al. (2004) and Zhao and Kockelman (2006) with DUE constraints and logit-based SUE constraints respectively, where the travel demand is not required for the calculation. The work of Yang et al. (2004) was recently extended by Han and Yang (2009) and Yang et al. (2009) using more effi- cient step sizes. Marginal cost pricing requires each link to be charged, thus it is not practical in real life. If one assumes that only a proportion of the network is charged, the second-best pricing scheme can be obtained (Yang et al., 2010). Second-best pricing problems can be formulated as a bi-level programming model, where the upper-level is to optimize any given system-wide index and the lower-level is a traffic assignment problem. The lower-level problem can be treated as a constraint on the upper-level problem, giving a form of mathematical programming with equilibrium constraints (MPEC) model (see, e.g., McDonald, 1995; Bellei et al., 2002; Chen and Bernstein, 2004, to name a few). The bi-level programming or MPEC model can be solved by various methods, including the iterative optimization-assignment algorithm (Allsop, 1974), equilibrium decomposed optimization (Suwansirikul et al., 1987), the sensitivity-analysis-based algorithm (Yang, 1997; Clark and Watling, 2002; Connors et al., 2007), the augmented Lagrangian algorithm (Meng et al., 2001) and gradient-based descent methods (Chiou, 2005). Although the cordon-based pricing scheme is a special type of second-best pricing, the aforementioned methods cannot be used for the speed-based toll design problem addressed in this study, due to the existence of continuously distributed VOT. As mentioned above, the VOT is used to convert the toll charges into time units so as to analyze the commuters’ route choice problem. The VOT is inherently influenced by many factors, including wage rate, time of day, trip purpose, importance of travel time reliability, etc. Thus, VOT can vary widely between commuters. It is thus more rational to take VOT to be a continuously distributed random variable across the population instead of assuming homogeneous network users with constant VOT or limited user classes with discrete VOTs (Han and Yang, 2008). Yet, studies of congestion pricing problems, or any other transportation network modeling problems, with continuously distributed VOT are quite scarce. Mayet and Hansen (2000) analyzed the toll design problem with continuous VOT on a network with two paths: one congested highway with a toll charge and one alternative path with a fixed travel cost. Expressions for the toll charge that maximizes the total user benefit were given by Mayet and Hansen, and they also discussed the effects of the distribution of VOT on the Pareto properties of the toll charge. Also based on the two-path example, Verhoef and Small (2004) investigated second-best pricing with a continuously distributed VOT. Xiao and Yang (2008) extended the work of Mayet and Hansen (2000) to cope with build-operate-transfer (BOT) contracts for highway franchising programs with continuously distributed VOT. Nie and Liu (2010) recently conducted a more in-depth analysis about the impacts of various distributions of VOT on the Pareto-improving congestion pricing scheme. However, these studies all rely on a network with two paths, while for a general transportation network with more than two paths, the findings may not apply. For a general transportation network, assuming the DUE principle, Leurent (1993) and Dial (1996, 1997) discussed the traffic assignment problem with continuously distributed VOT. Assuming probit-based SUE with fixed demand, Cantarella and Binetti (1998) later investigated a mathematical model and solution algorithm for the traffic assignment problem with continuously distributed VOT, using a path-based Monte Carlo simulation method to solve the stochastic network loading (SNL) problem. Meng et al. (2012) extended the work of Cantarella and Binetti (1998) by proposing a link-based Monte Carlo simulation method, which is modified and employed in this paper to solve the speed-based toll design problem with continuous VOT and SUE constraints. |