Estimation of origin‐destination matrices for a multimodal public transit network

This paper presents a procedure for the estimation of origin‐destination (O‐D) matrices for a multimodal public transit network. The system consists of a number of favored public transit modes that are obtained from a modal split process in a traditional four‐step transportation model. The demand of each favored mode is assigned to the multimodal network, which is comprised of a set of connected links of different public transit modes. An entropy maximization procedure is proposed to simultaneously estimate the O‐D demand matrices of all favored modes, which are consistent with target data sets such as the boarding counts and line segment flows that are observed directly in the network. A case study of the Hong Kong multimodal transit network is used to demonstrate the effectiveness of the proposed methodology.     

The marginal cost taxation of a transportation network

The paper shows that if the cost and demand functions satisfy certain weak smoothness conditions then the marginal cost taxation of a transportation network is optimal in the usual local sense. Interactions between the cost of travel along a link and flow along other links and between the demand for travel along a route and flow along other routes are permitted.     

Geographic information system design for network equilibrium-based travel demand models

Travel demand analyses are useful for transportation planning and policy development in a study area. However, travel demand modeling faces two obstacles. First, standard practice solves the four travel components (trip generation, trip distribution, modal split and network assignment) in a sequential manner. This can result in inconsistencies and non-convergence. Second, the data required are often complex and difficult to manage. Recent advances in formal methods for network equilibrium-based travel demand modeling and computational platforms for spatial data handling can overcome these obstacles. In this paper we report on the development of a prototype geographic information system (GIS) design to support network equilibrium-based travel demand models. The GIS design has several key features, including: (i) realistic representation of the multimodal transportation network, (ii) increased likelihood of database integrity after updates, (iii) effective user interfaces, and (iv) efficient implementation of network equilibrium solution algorithms.     

Joint optimization of a rail transit line and its feeder bus system

A model is developed for jointly optimizing the characteristics of a rail transit route and its associated feeder bus routes in an urban corridor. The corridor demand characteristics are specified with irregular discrete distributions which can realistically represent geographic variations. The total cost (supplier plus user cost) of the integrated bus and rail network is minimized with an efficient iterative method that successively substitutes variable values obtained through classical analytic optimization. The optimized variables include rail line length, rail station spacings, bus headways, bus stop spacings, and bus route spacing. Computer programs are designed for optimization and sensitivity analysis. The sensitivity of the transit service characteristics to various travel time and cost parameters is discussed. Numerical examples are presented for integrated transit systems in which the rail and bus schedules may be coordinated.     

Evaluation of exclusive bus lanes in a bi-modal degradable road network

In this paper, we proposed an evaluation method of exclusive bus lanes (EBLs) in a bi-modal degradable road network with car and bus transit modes. Link travel time with and without EBLs for two modes is analyzed with link stochastic degradation. Furthermore, route general travel costs are formulated with the uncertainty of link travel time for both modes and the uncertainty of waiting time at a bus stop and in-vehicle congestion costs for the bus mode. The uncertainty of bus waiting time is considered to be relevant to the degradation of the front links of the bus line. A bi-modal user equilibrium model incorporating travelers’ risk adverse behavior is proposed for evaluating EBLs. Finally, two numerical examples are used to illustrate how the road degradation level, travelers’ risk aversion level and the front link’s correlation level with the uncertainty of the bus waiting time affect the results of the user equilibrium model with and without EBLs and how the road degradation level affects the optimal EBLs setting scheme. A paradox of EBLs setting is also illustrated where adding one exclusive bus lane may decrease share of bus.     

Flexible feeder transit route design to enhance service accessibility in urban area

To improve the accessibility of transit system in urban areas, this paper presents a flexible feeder transit routing model that can serve irregular‐shaped networks. By integrating the cost efficiency of fixed‐route transit system and the flexibility of demand responsive transit system, the proposed model is capable of letting operating feeder busses temporarily deviate from their current route so as to serve the reported demand locations. With an objective of minimizing total bus travel time, a new operational mode is then proposed to allow busses to serve passengers on both street sides. In addition, when multiple feeder busses are operating in the target service area, the proposed model can provide an optimal plan to locate the nearest one to response to the demands. A three‐stage solution algorithm is also developed to yield meta‐optimal solutions to the problem in a reasonable amount of time by transforming the problem into a traveling salesman problem. Numerical studies have demonstrated the effectiveness of the proposed model as well as the heuristic solution approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Integrating geographic information systems into transit ridership forecast models

Researchers have produced sophisticated modal split and transit demand models, including forecasts that are sensitive to the level of service. However, little effort has been made to integrate these models into corridor studies and route alignment analyses since (a) re-routing is itself an extremely complex modeling task, and (b) the results of the demand models are presented in tabular form with no facility to visualize spatial patterns and relationships that, if recognized, would aid in the routing tasks. GIS tools can be used, together with the demand models, to identify both clusters of city blocks that house families with certain socioeconomic characteristics and potential trip destinations conducive to transit use. In other words, GIS tools can be used to better measure some of the factors that are needed by transit demand models. The results of these models can be displayed graphically, enabling analysts to target places needing improved service, evaluate route re-alignment alternatives, and operate more efficient and effective bus lines. This paper examines how a particular class of model used by transit agencies for estimating ridership can be integrated with GIS tools in order to facilitate such analyses. It also explores the effects of visualization of routes, demographics, and employment data on the process of designing route alignments with better targeting of high transit ridership areas. This paper is part of a research project sponsored by the Region One University Transportation Center, at MIT.     

Transportation serviceability analysis for metropolitan commuting corridors based on modal choice modeling

Major commuting corridors in metropolitan areas generally comprise multiple transportation modes for commuters, such as transit (subways or buses), private vehicles, or park-and-ride combinations. During the morning peak hour, the commuters would choose one of the available transportation modes to travel through the corridors from rural/suburban living areas to urban working areas. This paper introduces a concept of transportation serviceability to evaluate a transportation mode’s service status in a specific link, route, road, or network during a certain period. The serviceability can be measured by the possibility that travelers choose a specific type of transportation service at a certain travel cost. The commuters’ modal-choice possibilities are calculated using a stochastic equilibrium model based on general travel cost. The modeling results illustrate how transportation serviceability is influenced by background traffic flow in a corridor, value of comfort for railway mode, and parking fee distribution.     

A comparison of accuracy and cost of attribute data in analysis of transit service areas using GIS

User oriented transit service is designed to meet the particular needs of a selected group of travelers. Transit Routes are located to provide convenient linkages between user's origin and destination in such a way that out-of-vehicle time, such as access and transfer time, is minimized. Planning transit routes requires understanding demographics, land use and travel patterns in an area. The dynamic nature of these systems necessitates regular review and analysis to insure that the transit system continues to meet the needs of the area it serves. Geographic Information Systems (GIS) provide a flexible framework for planning and analyzing transit routes and stops. Socioeconomic, demographic, housing, land use, and traffic data may be modeled in a GIS to identify efficient and effective corridors to locate routes. Part of the route location and analysis problem requires estimating population within the service area of a route. A route's service area is defined using walking distance or travel time. The problem of identifying service areas for park and ride or auto/bus users is not considered here, but assumed analogous to walk/bus trips. This paper investigates the accuracy and costs associated with the use of different attribute data bases to perform service area analysis for transit routes using GIS. A case study is performed for Logan, Utah, where a new fixed route service is operated. The case study illustrates the use of census data, postal data, data collected from aerial photographs, and data collected during a field survey using the network area analysis technique for transit service area analysis. This comparison allows us to describe the amount of error introduced by various spatial modeling techniques of data bases representing a variety of aggregation levels.     

Exploring the influence of built environment on Uber demand

Ride-sourcing services have made significant changes to the transportation system, essentially creating a new mode of transport, arguably with its own relative utility compared to the other standard modes. As ride-sourcing services have become more popular each year and their markets have grown, so have the publications related to the emergence of these services. One question that has not been addressed yet is how the built environment, the so-called D variables (i.e., density, diversity, design, distance to transit, and destination accessibility), affect demand for ride-sourcing services. By having unique access to Uber trip data in 24 diverse U.S. regions, we provide a robust data-driven understanding of how ride-sourcing demand is affected by the built environment, after controlling for socioeconomic factors. Our results show that Uber demand is positively correlated with total population and employment, activity density, land use mix or entropy, and transit stop density of a census block group. In contrast, Uber demand is negatively correlated with intersection density and destination accessibility (both by auto and transit) variables. This result might be attributed to the relative advantages of other modes – driving, taking transit, walking, or biking – in areas with denser street networks and better regional job access. The findings of this paper have important implications for policy, planning, and travel demand modeling, where decision-makers seek solutions to shape the built environment in order to reduce automobile dependence and promote walking, biking, and transit use.     

Planning of integrated transit network for bus and LRT

An integrated approach is suggested for the planning and evaluation of mass transport systems which includes a bus network and LRT/RTS in urban areas. This approach involves a simplified procedure for determining mass transit demand, bus route network generation and evaluation, light or rapid transit corridor identification and its patronage determination in the presence of bus networks. Scheduling of a mass transportation system based on marginal ridership concept is also suggested for a given fleet size. All the three major components (demand estimation, route network generation and scheduling) iterate and interact each other with a feedback mechanism for the desired optimal solution in terms of performance indicators. Necessary interactive software packages for all the above subsystems have been developed.     

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.     

Integrated transit services for minimum cost operation considering heterogeneous demand

Abstract

In large metropolitan areas, public transit is a major mode choice of commuters for their daily travel, which has an important role in relieving congestion on transportation corridors. The purpose of this study is to develop a model which optimizes service patterns (SPs) and frequencies that yield minimum cost transit operation. Considering a general transit route with given stops and origin-destination demand, the proposed model consists of an objective total cost function and a set of constraints to ensure frequency conservation and sufficient capacity subject to operable fleet size. A numerical example is provided to demonstrate the effectiveness of the developed model, in which the demand and facility data of a rail transit route were given. Results show that the proposed model can be applied to optimize integrated SPs and headways that significantly reduce the total cost, while the resulting performance indicators are generated.     

A structural model of temporal change in multi-modal travel demand

A simultaneous equation model is developed to describe temporal trends and shifts in demand among five modes of passenger transportation in the Netherlands. The modes are car driver, car passenger, train, bicycle, and public transit (bus, tram, and subway). The time period is one year (1984–1985). The data are from the week-long travel diaries at six-month intervals of a national panel of households in the Netherlands. The model explains the weekly trip rates for each mode in terms of three types of relationships: links from demand for the same mode at previous points in time (temporal stability or inertia); links to and from demand for other modes at the same point in time (complementarity and competition on a synchronous basis); and links from demand for other modes at previous points in time (substitution effects). a significant model is found with 15 inertial links, 21 synchronous links, and 16 cross-lag links among the variables. It is proposed in interpretations of the link coefficients and overall effects of one variable on another that relationships among the modes are evolving over time. In particular, the model captures the effect of a public transit fare increase that occurred during the time frame of the panel data.     

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.     

Effects of queue spillover in networks considering simultaneous departure time and route choices

This paper explores the effects of queue spillover in transportation networks, in the context of dynamic traffic assignment. A model of spatial queue is defined to characterize dynamic traffic flow and queuing formation in network links. Network users simultaneously choose departure time and travel route to minimize the travel cost including journey time and unpunctuality penalty. Using some necessary conditions of the dynamic user equilibrium, dynamic network flows are obtained exactly on some networks with typical structure. Various effects of queue spillover are discussed based on the results of these networks, and some new paradoxes of link capacity expansion have been found as a result of such effects. Analytical and exact results in these typical networks show that ignoring queuing length may generate biased solutions, and the link storage capacity is a very important factor concerning the performance of networks.     

Data attribution and network representation issues in GIS and transportation

Geographic Information System (GIS) is an “intelligent” technology which integrates attribute data and spatial features and manages the relationship between them. GIS is widely used in many activities, but its application in transportation is less common. The extension of GIS into transportation (GIS‐T) offers the potential to integrate transportation data into GIS. The integration of transportation data in GIS faces a number of barriers that are discussed in the paper. These barriers can be broadly categorized into data attribution and network representation issues. This paper analyzes these issues and reviews the potential for overcoming these constraints with current GIS technology. A fully developed GIS‐T has to meet many diverse needs including transportation inventory, modeling and operational problems. If GIS is to succeed as a transportation technology, it must be capable of integrating different levels of network representation and data attribution and have the ability to link with other transportation technologies.     

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.     

Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study

The combination of increasing challenges in administering household travel surveys and advances in global positioning systems (GPS)/geographic information systems (GIS) technologies motivated this project. It tests the feasibility of using a passive travel data collection methodology in a complex urban environment, by developing GIS algorithms to automatically detect travel modes and trip purposes. The study was conducted in New York City where the multi-dimensional challenges include urban canyon effects, an extreme dense and diverse set of land use patterns, and a complex transit network. Our study uses a multi-modal transportation network, a set of rules to achieve both complexity and flexibility for travel mode detection, and develops procedures and models for trip end clustering and trip purpose prediction. The study results are promising, reporting success rates ranging from 60% to 95%, suggesting that in the future, conventional self-reported travel surveys may be supplemented, or even replaced, by passive data collection methods.     

An integrated Bayesian approach for passenger flow assignment in metro networks

This paper proposes an integrated Bayesian statistical inference framework to characterize passenger flow assignment model in a complex metro network. In doing so, we combine network cost attribute estimation and passenger route choice modeling using Bayesian inference. We build the posterior density by taking the likelihood of observing passenger travel times provided by smart card data and our prior knowledge about the studied metro network. Given the high-dimensional nature of parameters in this framework, we apply the variable-at-a-time Metropolis sampling algorithm to estimate the mean and Bayesian confidence interval for each parameter in turn. As a numerical example, this integrated approach is applied on the metro network in Singapore. Our result shows that link travel time exhibits a considerable coefficient of variation about 0.17, suggesting that travel time reliability is of high importance to metro operation. The estimation of route choice parameters conforms with previous survey-based studies, showing that the disutility of transfer time is about twice of that of in-vehicle travel time in Singapore metro system.