Pareto-improving ramp metering strategies for reducing congestion in the morning commute

This paper presents an alternative approach to internalize congestion externality during the morning commute. We consider a linear freeway with multiple on-ramps and a downstream bottleneck and commuters accessing the freeway via different on-ramps try to arrive at work on time. Rather than charging congestion tolls as widely suggested by economists, we show that the old-fashioned engineering approach – ramp metering – can be a powerful tool to affect travelers’ departure time choice and thereby alter the congestion externality distribution among travelers. With carefully designed time-dependent metering plans, travelers from different origins can be channelized and will access the freeway bottleneck in different time periods, resulting in less total cost for the system compared to the no-metering case. The metering strategies are Pareto-improving, with travelers from the on-ramp with the highest priority having the smallest individual costs and travelers from the on-ramp with the lowest priority having their costs equal to those in the no-metering scenario. By changing the priority order of the ramps periodically, the benefit of the Pareto-improving metering strategies can be distributed evenly among all travelers. Numerical experiments show that the total user cost can be reduced by up to 40% with the proposed metering strategies. This study offers researchers and policy makers a different angle of looking at congestion externality, and the results provide an overview of the potential long term benefits that dynamic ramp metering strategies can achieve.     

Joint road toll pricing and capacity development in discrete transport network design problem

The paper demonstrates a method to determine road network improvements that also involve the use of a road toll charge, taking the perspective of the government or authority. A general discrete network design problem with a road toll pricing scheme, to minimize the total travel time under a budget constraint, is proposed. This approach is taken in order to determine the appropriate level of road toll pricing whilst simultaneously addressing the need for capacity. The proposed approach is formulated as a bi-level programming problem. The optimal road capacity improvement and toll level scheme is investigated with respect to the available budget levels and toll revenues.     

Solving a discrete multimodal transportation network design problem

This paper investigates the multimodal network design problem (MMNDP) that optimizes the auto network expansion scheme and bus network design scheme in an integrated manner. The problem is formulated as a single-level mathematical program with complementarity constraints (MPCC). The decision variables, including the expanded capacity of auto links, the layout of bus routes, the fare levels and the route frequencies, are transformed into multiple sets of binary variables. The layout of transit routes is explicitly modeled using an alternative approach by introducing a set of complementarity constraints. The congestion interaction among different travel modes is captured by an asymmetric multimodal user equilibrium problem (MUE). An active-set algorithm is employed to deal with the MPCC, by sequentially solving a relaxed MMNDP and a scheme updating problem. Numerical tests on nine-node and Sioux Falls networks are performed to demonstrate the proposed model and algorithm.     

Step-tolling with price-sensitive demand: Why more steps in the toll make the consumer better off

Most dynamic models of congestion pricing use fully time-variant tolls. However, in practice, tolls are uniform over the day, or at most have just a few steps. Such uniform and step tolls have received surprisingly little attention from the literature. Moreover, most models that do study them assume that demand is insensitive to the price. This seems an empirically questionable assumption that, as this paper finds, strongly affects the implications of step tolling for the consumer. In the bottleneck model, first-best tolling has no effect on the generalised price, and thus consumer surplus remains the same as without tolling. Conversely, under price-sensitive demand, step tolling increases the price, making the consumer worse off. The more steps the toll has, the closer it approximates the first-best toll, thereby increasing the welfare gain and making consumers better off. This indicates the importance for real-world tolls to have as many steps as possible: this not only raises welfare, but may also increase the political acceptability of the scheme by making consumers better off.     

Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior

In order to reduce the number of vehicles stuck in congestion, especially for stop-and-go traffic at toll plazas, the establishment of electronic toll collection (ETC) systems has been a hot issue and dominant trend in many countries. Taiwan has joined the crowd, adding an ETC system to its toll roads in early 2006. However, despite the potential benefits for motorists, the utilization rate has been lower than expected during the introductory stage. The objective of this study is to advance our understanding on the critical antecedents of motorists’ intention of ETC service adoption by integrating both technology acceptance model (TAM) and theory of planned behavior (TPB) perspectives. Through empirical data collection and analysis from highway motorists who had not installed on-board units (OBU) for ETC service in Taiwan, we found that system attributes, perceived usefulness and perceived ease of use, indeed, positively engender motorists’ attitudes towards ETC service adoption. Moreover, results also reveal that attitude, subjective norm and perceived behavioral control positively influence the intention of ETC system adoption. Implications for practitioners and researchers, and suggestions for future research are also addressed in this study.     

加强收费站“四化”管理 努力构建和谐征缴关系

四化管理作为一种理念,对公路工作有着很强的指导性和针对性。实施四化管理,对实现收费公路科学发展、和谐发展和可持续发展有着深远的意义。构建和谐征缴关系,最关键就是处理好征费管理者与服务对象的关系。理顺征缴关系,就是要理顺缴费群体和内部事务关系。抓住以人为本,促进征缴双方和谐,才能实现通行费征收事业全面协调发展。     

Sustainability provisions in the bus-scheduling problem

The traditional vehicle scheduling problem attempts to minimize capital and operating costs. However, the carbon footprint and toxic air pollutants have become an increasingly important consideration. This paper studies the bus-scheduling problem and evaluates new types of buses that use alternative energy sources to reduce emissions, including some toxic air pollutants and carbon dioxide. A time-space network based approach is applied to formulate the problem to reduce the numbers of arcs in the underlying network; CPLEX is used to solve the problem. The results show that the bus-scheduling model can significantly reduce the bus emissions – hence reducing the carbon footprint of the transit operation – while only slightly increasing operating costs.     

Solution methods for the taxi pooling problem

In Taiwan, taxi pooling is currently performed by some taxi companies using a trial-and-error experience-based method, which is neither effective nor efficient. There is, however, little in the literature on effective models and solution methods for solving the taxi pooling problem. Thus, in this study we employ network flow techniques and a mathematical programming method to develop a taxi pooling solution method. This method is composed of three models. First, a fleet routing/scheduling model is constructed to produce fleet/passenger routes and schedules. A solution algorithm, based on Lagrangian relaxation, a sub-gradient method and a heuristic to find the upper bound of the solution, is proposed to solve the fleet routing/scheduling model. Then, two single taxi-passenger matching models are constructed with the goals of decreasing number of passenger transfers and matching all passengers and taxis. These two taxi-passenger matching models are directly solved using a mathematical programming solver. For comparison with the solution method, we also develop another heuristic by modifying a heuristic recently proposed for solving a one-to-many taxi pooling problem. The performance of the solution method and the additional heuristic are evaluated by carrying out a case study using real data and suitable assumptions. The test results show that these two solution methods could be useful in practice.     

Incorporating equity into the transit frequency-setting problem

This paper and the proposed formulation contribute to an apparent gap in transit research design by integrating equity considerations into the transit frequency-setting problem. The proposed approach provides a means to design transit service such that equitable access to basic amenities (e.g., employment, supermarkets, medical services) is provided for low-income populations or disadvantaged populations. The overarching purpose is to improve access via transit to basic amenities to: (1) reduce the disproportionate burden faced by transit dependent populations; and (2) create a more feasible transportation option for low-income households as an opportunity to increase financial security by reducing dependence on personal autos. The formulation is applied to data from a mid-sized US metropolitan area. The example application illustrates the formulation successfully increases access to employment opportunities for residents in areas with high percentages of low-income persons, as well as demonstrates the importance of considering uncertainty in the locations of populations and employment.     

More paradoxes in the equilibrium assignment problem

The sensitivity of travel costs to changes in input flows in the Wardrop equilibrium problem is studied. Examples are given showing that both origin to destination and global travel costs may decrease as a result of an increase in input flows. Other examples show that, in the two-mode equilibrium assignment problem transit origin to destination travel costs may decrease as a result of an increase in automobile input flows.     

Aggregation effects on the network design problem

This article examines the effects of various network extraction schemes on the network design problem. Given an original network, many criteria can be used to identify subnetworks on which the network design problem is solved. For the purposes of this article, these subnetworks are obtained using an extraction algorithm which preserves the magnitude of the user equilibrium flows on the links of these subnetworks. The results of the implementation of the network design problem on the original and the extracted subnetworks are presented and compared. We conclude that very good solutions to the network design problem can be obtained from the use of highly aggregate networks.     

On the dual approach to the traffic assignment problem

This paper attempts to explore the possibility of solving the traffic assignment problem with elastic demands by way of its dual problem. It is shown that the dual problem can be formulated as a nonsmooth convex optimization problem of which the objective function values and subgradients are conveniently calculated by solving shortest path problems associated with the transportation network. A subgradient algorithm to solve the dual problem is presented and limited computational experience is reported. The computational results are encouraging enough to demonstrate the effectiveness of the proposed approach.     

FMOLP approach to the inland water transportation problem

Fuzzy optimization techniques can be applied in determining the optimal schedule for the transport of gravel by inland water transportation. Gravel demand, for example, is difficult to determine precisely since it depends on the industrial development of the regions supplied by gravel and on possible buyers. The duration of the annual navigation period varies depending on the water level, possible icebergs, heavy fog, strong and frequent wind. The transport company is usually satisfied if total transportation costs stay within a reasonable range. The formulation of a linear programme lacks flexibility in dealing with imprecise input data. In this paper this type of problem has been approached with fuzzy optimization techniques.     

A statistical approach to the traveling salesman problem

A statistical approach is shown to be adaptable to the N-city traveling salesman problem by considering route distances to be random variables which are continuous and normally distributed. A solution to the shortest route distance and path can be approximated by utilizing a Monte Carlo simulation to obtain a representative sample of possible journeys. The approach involves recursive statistical inference which is used to select next-city visits leading to the most probable minimum route path. A statistical selection of the minimum route path is computationally efficient and computer run time increases in proportion to the square of the number of cities as opposed to an (N - 1)! increase for a deterministic approach. The accuracy of the statistical approach is directly proportional to the number of Monte Carlo simulations.     

Approximate algorithms for the discrete network design problem

The discrete network design problem is one of finding a set of feasible actions (projects) from among a collection of possible actions, that when implemented, optimizes some objective function(s). This is a combinatorial optimization problem that is very expensive to solve exactly. This paper proposes two algorithms for obtaining approximate solutions to the discrete network design problem with much less computational effeort. The computational savings are achieved by approximating the original problem with a new formulation which is easier to solve. The first algorithm proposed solves this approximate problem exactly, while the second is even more efficient, but provides only a near-optimal solution to the approximate problem. Experience with test problems indicates that these approximations can reduce the computational effort by a factor of 3–5, with little loss in solution accuracy.     

Simplical decomposition of the asymmetric traffic assignment problem

This paper presents a convergent simplicial decomposition algorithm for the variational inequality formulation of the asymmetric traffic assignment problem. It alternates between generating minimum path trees based on the cost function evaluated at the current iterate and the approximate solving of a master variational inequality subject to simple convexity constraints. Thus it generalizes the popular Frank-Wolfe method (where the master problem is a line search) to the asymmetric problem. Rules are given for dropping flow patterns which are not needed to express the current iterate as a convex combination of previous patterns. The results of some computational testing are reported.     

Formulating and solving the network design problem by decomposition

The optimal transportation network design problem is formulated as a convex nonlinear programming problem and a solution method based on standard traffic assignment algorithms is presented. The technique can deal with network improvements which introduce new links, which increase the capacity of existing links, or which decrease the free-flow (uncongested) travel time on existing links (with or without simultaneously increasing link capacity). Preliminary computational experience with the method demonstrates that it is capable of solving very large problems with reasonable amounts of computer time.     

Multi-state supernetwork framework for the two-person joint travel problem

Most travel behavior studies on route and mode choice focus only on an individual level. This paper adopts the concept of multi-state supernetworks to model the two-person joint travel problem (JTP). Travel is differentiated in terms of activity-vehicle-joint states, i.e. travel separately or jointly with which transport mode and with which activities conducted. In each state, route choice can be addressed given the state information and travel preference parameters. The joint travel pattern space is represented as a multi-state supernetwork, which is constructed by assigning the individual and joint networks to all possible states and connecting them via transfer links at joints where individuals can meet or depart. Besides route choice, the choices of where and when to meet, and which transport mode(s) to use can all be explicitly represented in a consistent fashion. A joint path through the supernetwork corresponds to a specific joint travel pattern. Then, JTP is reduced to an optimization problem to find the joint path with the minimum disutility. Three standard shortest path algorithm variants are proposed to find the optimal under different scenarios. The proposed framework further indicates the feasibility of multi-state supernetworks for addressing high dimensional problems and contributes to the design of a next generation of joint routing systems.     

An algorithm for the combined distribution and assignment problem

Much interest has recently been shown in the combination of the distribution and assignment models. In this paper we adopt a generalized Benders' decomposition to solve this combined problem for a system optimized assignment with linear link costs and explicit capacity constraints on link flows. The master problem which is generated is used to show that the combined problem can be viewed as a modified distribution problem, of gravity form, with a minimax instead of a linear objective function. An algorithm for solving the master problem is discussed, and some computational results presented.