Transport planning with multiple criteria: The analytic hierarchy process applications and progress review

Five examples of applications of the Analytic Hierarchy Process (AHP) are made to illustrate the different uses of this ratio scale multicriteria decision method in transportation. They include a commuter route selection hierarchy, a best mix of routes to Pittsburgh's new International Airport, a benefits/costs hierarchy to choose the best mode to cross a river, a planning hierarchy for a transport system and a simple dependence with feedback cycle to choose a car when criteria depend on the alternatives. For a better appreciation of the use and power of the method, the examples are followed by a resume of developments in research on the (AHP) in the last few years.     

A parallel algorithm for train rescheduling

One of the crucial factors in achieving a high punctuality in railway traffic systems, is the ability to effectively reschedule the trains when disturbances occur. The railway traffic rescheduling problem is a complex task to solve both from a practical and a computational perspective. Problems of practically relevant sizes have typically a very large search space, making them time-consuming to solve even for state-of-the-art optimization solvers. Though competitive algorithmic approaches are a widespread topic of research, not much research has been done to explore the opportunities and challenges in parallelizing them. This paper presents a parallel algorithm to efficiently solve the real-time railway rescheduling problem on a multi-core parallel architecture. We devised (1) an effective way to represent the solution space as a binary tree and (2) a novel sequential heuristic algorithm based on a depth-first search (DFS) strategy that quickly traverses the tree. Based on that, we designed a parallel algorithm for a multi-core architecture, which proved to be 10.5 times faster than the sequential algorithm even when run on a single processing core. When executed on a parallel machine with 8 cores, the speed further increased by a factor of 4.68 and every disturbance scenario in the considered case study was solved within 6 s. We conclude that for the problem under consideration, though a sequential DFS approach is fast in several disturbance scenarios, it is notably slower in many other disturbance scenarios. The parallel DFS approach that combines a DFS with simultaneous breadth-wise tree exploration, while being much faster on an average, is also consistently fast across all scenarios.     

The impacts of E-retail on the choice of shopping trips and delivery: Some preliminary findings

E-retail, like many other information technology-based activities (telecommuting, telemedicine etc.) offers a potential substitution of travel by telecommunications. Traditional shopping activities typically consist of a visit to a store in which product information is sought, and a decision on purchase is made. Pending that decision, the product is obtained and most often self-delivered by the consumer. Certain types of products are store-delivered to the consumer premises. In the face of E-retail, consumers can acquire information, make a purchase transaction and choose a delivery arrangement from a remote location. These options may result in a reduction of transport activity, as a delivery by the supplier is potentially more efficient than the traditional process. The current study presents a conceptual model of the decisions households make with regard to information gathering, purchase transactions and delivery mode. Data on revealed behavior and various socio-demographic and economic characteristics of shoppers was collected in the Tel-Aviv Metropolitan area in the summer of 2004.     

A multinomial logit car use model for a megacity of the developing world: Istanbul

The increase in motor vehicle use is one of the important factors that cause traffic congestion, especially in megacities. Thus, the reasons behind this increase require serious attention. This paper offers an analysis of this kind, for a megacity from the developing world, Istanbul. A stratified multinomial logit model accounting for the availability of a second vehicle in the household is estimated for a sample drawn from a questionnaire to gather information of actual car use in Istanbul. This estimation is only possible through a unique data generation process that converts actual preferences into a choice study setting. In addition, a simulation study, generally utilized in the analyses of discrimination between certain layers of society, and a scenario analysis related to changes in income are also included in the paper for a better understanding of the nature of the topic. The results show that the behavior of households with a second vehicle available and not available varies significantly due to household, individual and professional-related characteristics.     

On the modeling and solution algorithm for the reverse logistics recycling flow equilibrium problem

This paper presents a study that characterizes, formulates, and solves the reverse logistic recycling flow equilibrium (RLRFE) problem. The RLRFE problem is concerned with the recycling channel in which recyclable collectors, processors, landfills, and demand markets form a multi-tiered network to process the recycled material flows from sources destined either for landfills or demand markets. Motivated by a government policy making or enterprise conglomerate recycling system design and operation needs, the RLRFE problem is elaborated from a system-optimal perspective using the variational inequality (VI) approach. For each origin–destination (OD) pair, the corresponding equilibrium conditions are established as a variation of the Wardrop second principle. In light of demand and cost function interactions, a nested diagonalization solution (ND) algorithm is proposed that gradually transforms the RLRFE problem into a traffic assignment model. To address multiple landfills in the recycling network and to understand how a variable-demand problem can be analyzed as a fixed-demand problem, we propose a supernetwork representation of the RLRFE problem. A numerical analysis on a test case illustrates the model formulation and the proposed algorithm.     

Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries

This paper proposes neighborhood search heuristics to optimize the planned routes of vehicles in a context where new requests, with a pick-up and a delivery location, occur in real-time. Within this framework, new solutions are explored through a neighborhood structure based on ejection chains. Numerical results show the benefits of these procedures in a real-time context. The impact of a master–slave parallelization scheme, using an increasing number of processors, is also investigated.     

Constraint reformulation and a Lagrangian relaxation-based solution algorithm for a least expected time path problem

Using a sample-based representation scheme to capture spatial and temporal travel time correlations, this article constructs an integer programming model for finding the a priori least expected time paths. We explicitly consider the non-anticipativity constraint associated with the a priori path in a time-dependent and stochastic network, and propose a number of reformulations to establish linear inequalities that can be easily dualized by a Lagrangian relaxation solution approach. The relaxed model is further decomposed into two sub-problems, which can be solved directly by using a modified label-correcting algorithm and a simple single-value linear programming method. Several solution algorithms, including a sub-gradient method, a branch and bound method, and heuristics with additional constraints on Lagrangian multipliers, are proposed to improve solution quality and find approximate optimal solutions. The numerical experiments investigate the quality and computational efficiency of the proposed solution approach.     

Estimating the value of risk reductions for car drivers when pedestrians are involved: a case study in Spain

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality.     

A constraint generation algorithm for the construction of periodic railway timetables

This paper addresses the problem of constructing periodic timetables for train operations. We use a mathematical model consisting of periodic time window constraints by means of which arrival and departure times can be related pairwise on a clock, rather than on a linear time axis. Constructing a timetable, then, means solving a set of such constraints. This problem is known to be hard, i.e. it is NP-complete. We describe a new algorithm to solve the problem based on constraint generation and work out a real-life example. It appears that, for problem instances of modest, yet non-trivial, size, the algorithm performs very well, which opens a way to thorough performance analysis of railway systems by studying a large number of possible future timetables.     

Parameter transfer of common-metric attributes in choice analysis: implications for willingness to pay

There is a growing literature that promotes the presence of a mix of compensatory and semi-compensatory processing strategies in the way that individuals evaluate packages of attributes in real or hypothetical markets, and make choices. This paper proposes a specification for the utility form in a choice model to test if, given a pair of attributes with a common-metric (e.g., components of travel time or cost), the attribute with the dominating level defines the marginal (dis)utility that is assigned to both attributes. We refer to this processing strategy as a parameter transfer rule. We use a stated choice data set, in the context of car driving individuals choosing between tolled and non-tolled routes, to estimate a mixed logit model which incorporates the presence of the parameter transfer rule and the conventional fully compensatory rule, both existing up to a probability. We find that if this parameter transfer heuristic is part of the mix, the WTP is more than 30% higher, on average, than when only a fully compensatory rule is imposed. We also contrast the parameter transfer rule with other semi-compensatory heuristics which have been investigated in other papers, and show that the finding adds further support to the accumulating evidence that a semi-compensatory attribute processing rules tend to result in higher mean WTP estimates compared to the fully compensatory attribute processing rule.     

Multi-agent simulation of individual mobility behavior in carpooling

Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely; (i) create a motive to carpool, (ii) communicate this motive with other agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans, and (v) provide a feedback to all concerned agents. In this paper, we present a conceptual design of an agent-based model (ABM) for the carpooling a that serves as a proof of concept. Our model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our carpooling application, we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm, and a utility function to trigger the negotiation process between agents. We developed a prototype of our agent-based carpooling application based on the work presented in this paper and carried out a validation study of our results with real data collected in Flanders, Belgium.     

Shared taxis: modelling the choice of a paratransit mode in Santiago de Chile

The shared taxi is a special public transport mode, typical of Chilean cities. It operates with cars offering a maximum capacity of four seats, a predefined coverage area and a route that is fixed in principle, but can be adapted to meet passengers’ needs. During a normal day in Santiago, almost 700,000 trips use shared taxis during one of their stages. This represents about 4% of the total trips made in the city, and this modal share increases in zones and periods with low Metro and bus coverage. This study is a first attempt at studying shared taxis as a relevant transport alternative, analysing its main attributes and modelling its demand. With this purpose, after an analysis of the network and its operation, a revealed preference survey (including perceptual indicators) was applied to public transport users in Santiago who had shared taxi as a feasible alternative. Results show a positive evaluation of the mode’s unique attributes, such as the possibility of travelling seated, reducing transfers and alighting at a convenient destination. The subjective valuation of the attributes derived from the models confirm the strong penalty assigned by Chilean users to alternatives implying transfers or increased walking times. The analysis also shows that studying the characteristics of shared taxi users is relevant in a discussion about its regulation and modernization, considering that, while it is desirable to preserve its positive attributes, this should be done in a context of efficient integration with the rest of the transport system.

     

成品油管道穿越滑坡地段应力分析

成品油管道穿越山区滑坡地段时,受到滑坡推力的作用,易使管道产生过大变形而破坏。为探究此类管道安全问题并提出解决方案,使用应力分析软件CAESAR Ⅱ对某穿越滑坡地段的成品油管道进行应力及位移分析,得出其应力、位移分布,并分析纵向滑坡和横向滑坡对输油管道的影响。通过分析得出常规地段与滑坡地段的交界位置为输油管道的危险截面,且纵向滑坡对输油管道的应力及横向位移、纵向位移影响较大。     

Optimization of horizontal alignment geometry in road design and reconstruction

This paper presents a general formulation for optimization of horizontal road alignment, composed of tangential segments and circular curves suitably connected with transition curves (clothoids). It consists of a constrained optimization problem where the objective function is given by a line integral along the layout. The integrand is a function representing the cost of the road going through each point and, by considering different costs, a wide range of problems can be included in this formulation. To show it, we apply this methodology to three different situations. The two first cases are related with the design of a new road layout and used to solve a pair of academic examples. The third problem deals with the improvement of a road adapting the old path to current legislation, and it is solved taking as case study the reconstruction project for a regional road (NA-601) in the north of Spain.     

A graph-theoretic method to quantify the airline route authority

A U.S. certificated passenger airline must be authorized by the Civil Aeronautics Board before it can schedule a route to serve a set of cities. The route authority is described in a number of legal statements on a Route Certificate, which specifies the routing restrictions by which the airline must abide in offering the service. For cost considerations, an airline is often interested in the shortest flight-time routes between city pairs. This paper introduces a graph-theoretic method to quantify the legal statements in a Certificate. Through straightforward matrix operations, all the authorized nonstop and multistop routes, including the shortest time routes, can be generated. The method is a convenient tool to help an airline to generate alternative network routes as demonstrated by a case study of American Airlines.     

A hybrid Genetic Algorithm approach to minimize the total joint cost of a single-vendor multi-customer integrated scheduling problem

This paper addresses the scheduling of supply chains with interrelated factories consisting of a single vendor and multiple customers. In this research, one transporter is available to deliver jobs from vendor to customers, and the jobs can be processed by batch. The problem studied in this paper focuses on a real-case scheduling problem of a multi-location hospital supplied with a central pharmacy. The objective of this work is to minimize the total cost, while satisfying the customer’s due dates constraints. A mathematical formulation of the problem is given as a Mixed Integer Programming model. Then, a Branch-and-Bound algorithm is proposed as an exact method for solving this problem, a greedy local search is developed as a heuristic approach, and a hybrid Genetic Algorithm is presented as a meta-heuristic. Computation experiments are conducted to highlight the performance of the proposed methods.     

Effects of increased awareness on choice of travel mode

This paper reports a field experiment with the purpose of studying the effects of increased awareness on travel mode choice. One hundred fifteen subjects were randomly assigned to an experimental and a control group. In the experimental group, a more deliberate choice of travel mode was induced and expected to result in a stronger relationship between attitude and behavior, a weaker relationship between habit and behavior, and a behavioral change among individuals with a strong habit. Attitude, habit, and behavior were measured in travel diaries and questionnaires. The results indicated no significant change in the relationship between attitude and behavior and no significant change in the relationship between habit and behavior. However, a temporally extended decrease in car use was observed in the experimental group. The effect was noted for individuals with a strong habit who reduced their car use but not for subjects with a weak habit.     

基于管片错台的通缝拼装盾构隧道纵向变形安全评估

以上海软土地区通缝拼装地铁盾构隧道为研究对象,根据隧道管片环缝接头处的详细构造,对管片错台模式下的隧道纵向变形进行了研究,建立了隧道纵向结构变形安全评估方法,确定了相应的控制准则和评估流程,形成了一套隧道结构纵向变形的安全评估体系.     

Designing a home-to-work bus service in a metropolitan area

This paper presents a model and an algorithm for the design of a home-to-work bus service in a metropolitan area. This type of service must display an equilibrium between conflicting criteria such as efficiency, effectiveness, and equity. To this end, we introduce a multi-objective model in which, among other aspects, equity is considered by time windows on the arrival time of a bus at a stop. Time windows can have other uses such as, for example, guaranteeing synchronization of the service with other transportation modes. This is one of the guiding principles of the proposed model which is based on concepts that simultaneously tackle several issues at once. Along this line, we propose a cluster routing approach to model both bus stop location and routing in urban road networks where turn restrictions exist. The resulting multi-objective location-routing model is solved by a tabu search algorithm. As an application, we analyze a home-to-work bus service for a large research center located in Rome, Italy. This case study provides a benchmark for the algorithmic results, and shows the practical relevance of the proposed methodology.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.