Real-time partway deadheading strategy based on transit service reliability assessment

This paper presents a partway deadheading strategy for transit operations to improve transit service of the peak directions of transit routes. This strategy consists of two phases: reliability assessment of further transit service and optimization of partway deadheading operation. The reliability assessment of further transit service, which is based on the current and recent service reliability, is used to justify whether or not to implement a partway deadheading operation. The objective of the second phase is to determine the beginning stop for a new service for the deadheaded vehicle by maximizing the benefit of transit system. A heuristic algorithm is also defined and implemented to estimate reliability of further transit service and to optimize partway deadheading operation. Then, the partway deadheading strategy proposed in this paper is tested with the data from a transit route in Dalian city of China. The results show the partway deadheading strategy with the reasonable parameters can improve transit service.     

A traffic flow simulator for network reliability assessment

This paper investigates the transportation network reliability based on the information provided by detectors installed on some links. A traffic flow simulator (TFS) model is formulated for assessing the network reliability (in terms of travel time reliability), in which the variation of perceived travel time error and the fluctuations of origin-destination (OD) demand are explicitly considered. On the basis of prior OD demand and partial updated detector data, the TFS can estimate the link flows for the whole network together with link/path travel times, and their variance and covariance. The travel time reliability by OD pair can also be assessed and the OD matrix can be updated simultaneously. A Monte Carlo based algorithm is developed to solve the TFS model. The application of the proposed TFS model is illustrated by a numerical example.     

Validation of a schedule‐based capacity restraint transit assignment model for a large‐scale network

This paper describes the application of a capacity restraint trip assignment algorithm to a real, large‐scale transit network and the validation of the results. Unlike the conventional frequency‐based approach, the network formulation of the proposed model is dynamic and schedule‐based. Transit vehicles are assumed to operate to a set of pre‐determined schedules. Passengers are assumed to select paths based on a generalized cost function including in‐vehicle and out‐of‐vehicle time and line change penalty. The time‐varying passenger demand is loaded onto the network by a time increment simulation method, which ensures that the capacity restraint of each vehicle during passenger boarding is strictly observed. The optimal‐path and path‐loading algorithms are applied iteratively by the method of successive averages until the network converges to the predictive dynamic user equilibrium. The Hong Kong Mass Transit Railway network is used to validate the model results. The potential applications of the model are also discussed.     

A schedule‐based dynamic transit network model — Recent advances and prospective future research

Using the schedule‐based approach, in which scheduled timetables are used to describe the movement of vehicles, a dynamic transit assignment model is formulated. Passengers are assumed to travel on a path with minimum generalized cost that consists of four components: in‐vehicle time; waiting time; walking time; and a time penalty for each line change. A specially developed branch and bound algorithm is used to generate the time‐dependent minimum path. The assignment procedure is conducted over a period in which both passenger demand and train headway are varying. This paper presents an overview of the research that has been carried out by the authors to develop the schedule‐based transit assignment model, and offers perspectives for future research.     

Partition‐based algorithm for estimating transportation network reliability with dependent link failures

Evaluating the reliability of a transportation network often involves an intensive simulation exercise to randomly generate and evaluate different possible network states. This paper proposes an algorithm to approximate the network reliability which minimizes the use of such simulation procedure. The algorithm will dissect and classify the network states into reliable, unreliable, and un‐determined partitions. By postulating the monotone property of the reliability function, each reliable and/or unreliable state can be used to determine a number of other reliable and/or unreliable states without evaluating all of them with an equilibrium assignment procedure. The paper also proposes the cause‐based failure framework for representing dependent link degradation probabilities. The algorithm and framework proposed are tested with a medium size test network to illustrate the performance of the algorithm.     

A reliability‐based network design problem

This paper presents a reliability‐based network design problem. A network reliability concept is embedded into the continuous network design problem in which travelers' route choice behavior follows the stochastic user equilibrium assumption. A new capacity‐reliability index is introduced to measure the probability that all of the network links are operated below their capacities when serving different traffic patterns deviating from the average condition. The reliability‐based network design problem is formulated as a bi‐level program in which the lower level sub‐program is the probit‐based stochastic user equilibrium problem and the upper level sub‐program is the maximization of the new capacity reliability index. The lower level sub‐program is solved by a variant of the method of successive averages using the exponential average to represent the learning process of network users on a daily basis that results in the daily variation of traffic‐flow pattern, and Monte Carlo stochastic loading. The upper level sub‐program is tackled by means of genetic algorithms. A numerical example is used to demonstrate the concept of the proposed framework.     

Measuring network reliability: A game theoretic approach

Despite the importance of assessing the reliability of transport networks in general there is a paucity of suitable techniques. In part this is due to the fact that network performance depends both on the state of the infrastructure and on the behaviour of network users, where user behaviour is governed by expectations about the state of the network. An approach based on game theory is proposed whereby the performance of the network is estimated for the case where network users are extremely pessimistic about the state of the network. Where the routes are prespecified and route utilities depend only on exogenously given scenarios, the estimation problem may be formulated as a linear program. A reformulation of the problem as a non-linear program allows the impact of the degree of user pessimism on expected network utility to be studied. The problems of implementing the method for large networks with multiple origins and destinations is discussed and an algorithm is proposed.     

Estimation of a time‐dependent origin‐destination matrix for congested highway networks

This paper presents a time‐dependent origin‐destination (O‐D) matrix estimation procedure embedded with a dynamic traffic assignment model, in which the predictive dynamic user optimal conditions in congested networks are maintained. Two solution algorithms are proposed, namely: an iterative (ITR) scheme and a method of successive averages (MSA) scheme. It is found that the MSA scheme outperforms the ITR scheme. As a prior O‐D matrix is an important input for the problem, its quality is essential for the reliability of the matrix estimation procedure. Empirical constraints are set in relation to the quality of the prior O‐D matrix for the estimation procedure. Numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

Network equilibrium for congested multi‐mode networks with elastic demand

This paper proposes an elastic demand network equilibrium model for networks with transit and walking modes. In Hong Kong, the multi‐mode transit system services over 90% of the total journeys and the demand on it is continuously increasing. Transit and walking modes are related to each other as transit passengers have to walk to and from transit stops. In this paper, the multi‐mode elastic‐demand network equilibrium problem is formulated as a variational inequality problem where the combined mode and route choices are modeled in a hierarchical logit structures and the total travel demand for each origin‐destination pair is explicitly given by an elastic demand function. In addition, the capacity constraint for transit vehicles and the effects of bi‐directional flows on walkways are considered in the proposed model. All these congestion effects are taken into account for modeling the travel choices. A solution algorithm is developed to solve the multi‐mode elastic‐demand network equilibrium model. It is based on a Block Gauss‐Seidel decomposition approach coupled with the method of successive averages. A numerical example is used to illustrate the application of the proposed model and solution algorithm.     

A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty

An understanding of the interaction between individuals’ activities and travel choice behaviour plays an important role in long-term transit service planning. In this paper, an activity-based network equilibrium model for scheduling daily activity-travel patterns (DATPs) in multi-modal transit networks under uncertainty is presented. In the proposed model, the DATP choice problem is transformed into a static traffic assignment problem by constructing a new super-network platform. With the use of the new super-network platform, individuals’ activity and travel choices such as time and space coordination, activity location, activity sequence and duration, and route/mode choices, can be simultaneously considered. In order to capture the stochastic characteristics of different activities, activity utilities are assumed in this study to be time-dependent and stochastic in relation to the activity types. A concept of DATP budget utility is proposed for modelling the uncertainty of activity utility. An efficient solution algorithm without prior enumeration of DATPs is developed for solving the DATP scheduling problem in multi-modal transit networks. Numerical examples are used to illustrate the application of the proposed model and the solution algorithm.     

Passenger route guidance system for multi‐modal transit networks

In many public transport oriented cities in the world, especially Asian cities, the public transport system has been developed extensively, to the extent that it has become increasingly difficult to navigate. Although inter‐modal transfers are common and often necessary, a complete set of the routes across transport modes is generally not presented in a form that is accessible to travelers, as each operator would only publish its own routes. Moreover, the common nonlinear fare tables together with inter‐modal fare discounts pose challenges to travelers in deciding their best routes. This study develops a multi‐modal passenger route guidance system, called eFinder, to aid travelers with their combined mode‐route choices. We discuss the architecture and features of this system in this study. This system forms a platform for disseminating public transit information and should complement further development and use of the public transport network by enabling travelers to make informed choices.     

An activity-based approach for scheduling multimodal transit services

This paper proposes a new activity-based transit assignment model for investigating the scheduling (or timetabling) problem of transit services in multi-modal transit networks. The proposed model can be used to generate the short-term and long-term timetables of multimodal transit lines for transit operations and service planning purposes. The interaction between transit timetables and passenger activity-travel scheduling behaviors is captured by the proposed model, as the activity and travel choices of transit passengers are considered explicitly in terms of departure time choice, activity/trip chain choices, activity duration choice, transit line and mode choices. A heuristic solution algorithm which combines the Hooke–Jeeves method and an iterative supply–demand equilibrium approach is developed to solve the proposed model. Two numerical examples are presented to illustrate the differences between the activity-based approach and the traditional trip-based method, together with comparison on the effects of optimal timetables with even and uneven headways. It is shown that the passenger travel scheduling pattern derived from the activity-based approach is significantly different from that obtained by the trip-based method, and that a demand-sensitive (with uneven headway) timetable is more efficient than an even-headway timetable.     

The importance of time in transit and reliability of transit time for shippers,receivers, and carriers

Reliability of transit time is reputed to be the most important variable influencing freight transport today, according to shipper surveys. Average transit time also plays a major role. A model is developed that shows how a cost-minimizing shipper will adjust its economic order quantity as reliability and/or time in transit changes. Such changes impact on average inventory costs, ordering costs, expected shortage costs and expected excess costs. The model is developed for both discrete and continuous transit time distributions. Reliability is defined as the variance of transit time. A matrix is prepared for some sample data, which shows the minimum cost attainable with each mean/variance of transit time distribution. Comparing across rows and columns of the matrix enables one to show the value (reduction in total cost) obtainable by improving reliability and/or mean transit time. In addition, value can be obtained by improving reliability while increasing average transit time. It is suggested that the model can be used for shippers in negotiating service improvements with carriers and by carriers in negotiating service improvements with shippers. In the former case, the carrier can determine how much they are willing to pay for the improvement, whereas in the latter case, the carriers can determine how much they are able to charge for the improvement.     

Subsidy and public transit performance: A factor analytic approach

The need to measure and evaluate transit system performance has led to the development of numerous performance indicators. However, depending upon the indicator, we oftentimes reach different conclusions regarding transit system performance. The research reported in this paper uses factor analytic methods to generate a set of underlying attributes (factors) that capture the performance of public transit systems in Indiana. Similar to what is reported in the literature, this study finds three attributes that best describe transit system performance: efficiency, effectiveness, and overall performance. Based upon systemsÕ factor scores, the study finds that systems scoring highly on one attribute generally perform well on the remaining attributes. Further, there is an inverse relationship between system performance and subsidies, a finding that supports performance based subsidy allocations.     

An investigation of the reliability of real-time information for route choice decisions in a congested traffic system

This paper investigates the reliability of information on prevailing trip times on the links of a network as a basis for route choice decisions by individual drivers. It considers a type of information strategy in which no attempt is made by some central controller or coordinating entity to predict what the travel times on each link would be by the time it is reached by a driver that is presently at a given location. A specially modified model combining traffic simulation and path assignment capabilities is used to analyze the reliability of the real-time information supplied to the drivers. This is accomplished by comparing the supplied travel times (at the link and path levels) to the actual trip times experienced in the network after the information has been given. In addition, the quality of the decisions made by drivers on the basis of this information (under alternative path switching rules) is evaluated ex-post by comparing the actually experienced travel time (given the decision made) to the time that the driver would have experienced without the real-time information. Results of a series of simulation experiments under recurrent congestion conditions are discussed, illustrating the interactions between information reliability and user response.     

A multiple criteria approach for the evaluation of the rail transit networks in Istanbul

The deficiencies in the Istanbul transportation system have led the local authorities to plan several alternative transportation projects. In this paper three alternative rail transit network proposals are evaluated by using Analytic Hierarchy Process (AHP), a multiple criteria decision support system. The AHP facilitates decision-making by organizing perceptions, experiences, knowledge and judgments, the forces that influence the decision, into a hierarchical framework with a goal, scenarios, criteria and alternatives of choice. Based on this analysis, the decision makers have developed a new alternative as a combination of the most closely competing two alternative rail transit networks. This combination rail transit network is currently under construction.     

An AI-based approach for transit route system planning and design

We present an AI-based solution approach to the transit network design problem (TNDP). Past approaches fall into three categories: optimization formulations of idealized situations, heuristic approaches, or practical guidelines and ad hoc procedures reflecting the professional judgement and practical experience of transit planners. We discuss the sources of complexity of the TNDP as well as the shortcomings of the previous approaches. This discussion motivates the need for AI search techniques that implement the existing designer's knowledge and expertise to achieve better solutions efficiently. Then we propose a hybrid solution approach that incorporates the knowledge and expertise of transit network planners and implements efficient search techniques using AI tools, algorithmic procedures developed by others, and modules for tools implemented in conventional languages. The three major components of the solution approach are presented, namely, the lisp-implemented route generation design algorithm (RGA), the analysis procedure TRUST (Transit Route Analyst), and the route improvement algorithm (RIA). An example illustration is included.     

Dispatching control at transfer stations in multi‐hub transit networks

Among dispatching control approaches, the holding option has attracted the most attention in bus control. However, holding a vehicle at a transfer station may exacerbate the delays because more passengers might accumulate at downstream stations and may also affect other connecting routes at other transfer stations. Our problem is to minimize the total costs of dispatching ready vehicles at each transfer station along coordinated routes in a multi‐hub transit network. The total costs include the waiting cost for on‐board passengers, the missed connection costs for late arrival passengers at the subject transfer station and possible transfer costs at downstream transfer stations. We develop a heuristic algorithm to optimize the holding times based on real time information about late vehicles. The results show that ready vehicles should be held longer when the arrival variances of late vehicles are small or when many late connecting passengers are expected.