Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach

This paper assesses the demand for a flexible, demand-adaptive transit service, using the Chicago region as an example. We designed and implemented a stated-preference survey in order to (1) identify potential users of flexible transit, and (2) inform the service design of the flexible transit mode. Multinomial logit, mixed-logit, and panel mixed-logit choice models were estimated using the data obtained from the survey. The survey instrument employed a d_p-efficient design and the Google Maps API to capture precise origins and destinations in order to create realistic choice scenarios. The stated-preference experiments offered respondents a choice between traditional transit, car, and a hypothetical flexible transit mode. Wait time, access time, travel time, service frequency, cost, and number of transfers varied across the choice scenarios. The choice model results indicate mode-specific values of in-vehicle travel time ranging between $16.3 per hour (car) and $21.1 per hour (flexible transit). The estimated value of walking time to transit is $25.9 per hour. The estimated value of waiting time at one’s point of origin for a flexible transit vehicle is $11.3 per hour; this value is significantly lower than the disutility typically associated with waiting at a transit stop/station indicating that the ‘at-home’ pick-up option of flexible transit is a highly desirable feature. The choice model results also indicate that respondents who use active-transport modes or public transit for their current commute trip, or are bikeshare members, were significantly more likely to choose flexible and traditional transit than car commuters in the choice experiments. The implications of these and other relevant model results for the design and delivery of flexible, technology-enabled services are discussed.     

A causal analysis of car ownership and transit use

The causal structure underlying household mobility is examined in this study using a sample obtained from the Dutch National Mobility Panel survey. The results indicate that car ownership is strongly associated with mode use, but that it has no influence on weekly person trip generation by household members. Characteristics of mode use are examined through a causal analysis of changes in car ownership, number of drivers, number of car trips, and number of transit trips. It is shown that observed changes in mode use cannot be adequately explained by assuming that a change in transit use influences car use. The finding suggests that the increase in car use, which is a consequence of increasing car ownership, may not be suppressed by improving public transit.     

Equity-oriented skip-stopping schedule optimization in an oversaturated urban rail transit network

In this study, we focus on improving system-wide equity performance in an oversaturated urban rail transit network based on multi-commodity flow formulation. From the system perspective, an urban rail transit network is a distributed system, where a set of resources (i.e., train capacity) is shared by a number of users (i.e., passengers), and equitable individuals and groups should receive equal shares of resources. However, when oversaturation occurs in an urban rail transit network during peak hours, passengers waiting at different stations may receive varying shares of train capacity leading to the inequity problem under train all-stopping pattern. Train skip-stopping pattern is an effective operational approach, which holds back some passengers at stations and re-routes their journeys in the time dimension based on the available capacity of each train. In this study, the inequity problem in an oversaturated urban rail transit network is analyzed using a multi-commodity flow modeling framework. In detail, first, discretized states, corresponding to the number of missed trains for passengers, are constructed in a space-time-state three-dimensional network, so that the system-wide equity performance can be viewed as a distribution of all passengers in different states. Different from existing flow-based optimization models, we formulate individual passenger and train stopping pattern as commodity and network structure in the multi-commodity flow-modeling framework, respectively. Then, we aim to find an optimal commodity flow and well-designed network structure through the proposed multi-commodity flow model and simultaneously achieve the equitable distribution of all passengers and the optimal train skip-stopping pattern. To quickly solve the proposed model and find an optimal train skip-stopping pattern with preferable system-wide equity performance, the proposed linear programming model can be effectively decomposed to a least-cost sub-problem with positive arc costs for each individual passenger and a least-cost sub-problem with negative arc costs for each individual train under a Lagrangian relaxation framework. For application and implementation, the proposed train skip-stopping optimization model is applied to a simple case and a real-world case based on Batong Line in the Beijing Subway Network. The simple case demonstrates that our proposed Lagrangian relaxation framework can obtain the approximate optimal solution with a small-gap lower bound and a lot of computing time saved compared with CPLEX solver. The real-world case based on Batong Line in the Beijing Subway Network compares the equity and efficiency indices under the operational approach of train skip-stopping pattern with those under the train all-stopping pattern to state the advantage of the train skip-stopping operational approach.     

Learning to use transit services: adapting to unfamiliar transit travel

Transportation - Travel behavior change has become an area of interest in many cities around the world, particularly to encourage people to change from car use to transit use. Previous research...     

Disentangling the role of cars and transit in employment and labor earnings

We examine the relationship between transportation access on the one hand and individuals’ employment and labor earnings on the other. We improve on existing studies by bringing a large national panel data set to bear on this question, attempting to disentangle the mechanisms by which individuals improve their economic standing and, finally, comparing the economic benefits to the direct costs of car ownership. To do this, we use nine waves from the Panel Study of Income Dynamics from 1999 to 2015. We find that access to a car is a strong predictor of future economic benefit for individuals, and that at very high levels of transit access, carless individuals can also fare equally well. Access to an automobile is strongly associated with employment, job retention, and earning more money over time. Though having a car is associated with economic benefits, owning and operating a car is expensive; yet, our findings suggest that the benefits may outweigh the costs for most people living outside neighborhoods with truly excellent transit service.

     

Measuring the satisfaction of multimodal travelers for local transit services in different urban contexts

The importance of measuring customer satisfaction for a public transport service is apparent, even beyond more immediate marketing purposes. The present paper shows how satisfaction measures can be exploited to gain insights on the relationship between personal attitudes, transit use and urban context. We consider nine satisfaction measures of urban transit services, as expressed by a representative sample of Italian multimodal travelers (i.e. users of both private cars and public transport). We use correlations and correspondence analyses to show if and how each attribute is related to the levels of use of public transport, and how the relationship is affected by the urban context. Then we apply a recently developed method to combine ordinal variables into one score, by adapting it to work with large samples and with satisfaction measures which have a neutral point in the scale (i.e. “neither satisfied nor dissatisfied”). The resulting overall satisfaction levels and frequency of use were not correlated in our sample. We also found the highest satisfaction levels in smaller towns and the lowest ones in metropolitan cities. Since we focus on multimodal travelers, an interpretation paradigm is proposed according to which transit services must be well evaluated by car drivers in smaller towns in order to be considered a real alternative to cars. On the other hand, transit is more competitive on factual elements in larger cities, so that it can still be used by drivers, even if it is not very well evaluated.     

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.     

The impact of transit station areas on the travel behaviors of workers in Denver,Colorado

Transit development is one planning strategy that seeks to partially overcome limitations of low-density single use car oriented development styles. While many studies focus on how residential proximity to transit influences the travel behaviors of individuals, the effect of workplace proximity to transit is less understood. This paper asks, does working near a light rail transit station influence the travel behaviors of workers differently than workers living near a station? We begin by examining workers’ commute mode based on their residential and workplace proximity to transit station areas. Next, we analyze the ways in which personal travel behaviors differ between those who drive to work and those who do not. The data came from a 2009 travel behavior survey in the Denver, Colorado metropolitan area, which contains 8000 households, 16,000 individuals, and nearly 80,000 trips. We measure sustainable travel behaviors as reduced mileage, reduced number of trips, and increased use of non-car transportation. The results of this study indicate that living near a transit station area by itself does not increase the likelihood of using non-car modes for work commutes. But if the destination (work) is near a transit station area, persons are less likely to drive a car to work. People who both live and work in a transit station area are less likely to use a car and more likely to take non-car modes for both work and non-work (personal) trips. Especially for persons who work near a transit station area, the measures of personal trips and distances show a higher level of mobility for non-car commuters than car commuters – that is, more trips and more distant trips. The use of non-car modes for personal trips is most likely to occur by non-car commuters, regardless of their transit station area relationship.     

Ridership estimation procedure for a transit corridor with new bus rapid transit service

Ridership estimation is a critical step in the planning of a new transit route or change in service. Very often, when a new transit route is introduced, the existing routes will be modified, vehicle capacities changed, or service headways adjusted. This has made ridership forecasts for the new, existing, and modified routes challenging. This paper proposes and demonstrates a procedure that forecasts the ridership of all transit routes along a corridor when a new bus rapid transit (BRT) service is introduced and existing regular bus services are adjusted. The procedure uses demographic data along the corridor, a recent origin–destination survey data, and new and existing transit service features as inputs. It consists of two stages of transit assignment. In the first stage, a transit assignment is performed with the existing transit demand on the proposed BRT and existing bus routes, so that adjustments to the existing bus services can be identified. This transit assignment is performed iteratively until there is no adjustment in transit services. In the second stage, the transit assignment is carried out with the new BRT and adjusted regular bus services, but incorporates a potential growth in ridership because of the new BRT service. The final outputs of the procedure are ridership for all routes and route segments, boarding and alighting volumes at all stops, and a stop‐by‐stop trip matrix. The proposed ridership estimation procedure is applicable to a new BRT route with and without competing regular bus routes and with BRT vehicles traveling in dedicated lanes or in mixed traffic. The application of the proposed procedure is demonstrated via a case study along the Alameda Corridor in El Paso, Texas. Copyright © 2015 John Wiley & Sons, Ltd.     

A simulation model of train travel on a rail transit line

A simplified simulation model for the operational analysis of a rail rapid transit train is presented. The model simulates the movement of a train along a route, and develops the relationships of time—distance, time—speed and distance—speed. The inputs to the model are the profile of speed limits and the dynamic characteristics of the train. Without the information on the track geometry and tractive effort, the model determines the speed of the train at a location based on the previous and future speed limits relative to the location. It was found that the model can fairly accurately simulate the relationship between travel time and distance. A comparison of the train travel times between the actual and simulated runs is presented. Because of the simplicity of input and calculation method, the model can be a useful tool for the “desk-top” analysis of frequently occurring planning problems of a commuter rail or rail rapid transit line, such as the impacts of changes in speed limits, station locations, station stopping policy, addition/elimination of stations, and types of rail cars.     

Can value capture work in a car dependent city? Willingness to pay for transit access in Perth,Western Australia

This paper investigates the impact of transit on urban land markets in the highly car dependent corridors of Perth with a focus on where new fast rail transit services have recently been built. It determines people’s willingness to pay for transit access within different pedestrian catchments for each of the corridors based on hedonic price modelling using land value data on over 460,000 households. The case study uses cross sectional and panel data hedonic price modelling methodology for the calculation of willingness to pay for transit. It finds that land market increases of up to 40% can be achieved, and is particularly relevant to car dependent cities looking to capture the financial and economic value created to build transit extensions or entirely new systems, thus making a strong case for value capture funding of transit projects into car dependent suburbs and the potential for density increases near stations.     

Mesoscopic simulation for transit operations

This paper presents a transit simulation model designed to support evaluation of operations, planning and control, especially in the context of Advanced Public Transportation Systems (APTS). Examples of potential applications include frequency determination, evaluation of real-time control strategies for schedule maintenance and assessing the effects of vehicle scheduling on the level of service. Unlike most previous efforts in this area, the simulation model is built on a platform of a mesoscopic traffic simulation model, which allows modeling of the operation dynamics of large-scale transit systems taking into account the stochasticity due to interactions with road traffic. The capabilities of Mezzo as an evaluation tool of transit operations are demonstrated with an application to a real-world high-demand bus line in the Tel-Aviv metropolitan area under various scenarios. The headway distributions at two stops are compared with field observations and show good consistency between simulated and observed data.     

A slack arrival strategy to promote flex-route transit services

Flex-route transit, which combines the advantages of fixed-route transit and demand-responsive transit, is one of the most promising options in low-demand areas. This paper proposes a slack arrival strategy to reduce the number of rejected passengers and idle time at checkpoints resulting from uncertain travel demand. This strategy relaxes the departure time constraints of the checkpoints that do not function as transfer stations. A system cost function that includes the vehicle operation cost and customer cost is defined to measure system performance. Theoretical and simulation models are constructed to test the benefits of implementing the slack arrival strategy in flex-route transit under expected and unexpected demand levels. Experiments over a real-life flex-route transit service show that the proposed slack arrival strategy could improve the system performance by up to 40% with no additional operating cost. The results demonstrate that the proposed strategy can help transit operators provide more cost-efficient flex-route transit services in suburban and rural areas.     

An analysis of public bus transit performance in Indian cities

Maintaining and enhancing public transit service in Indian cities is important, to meet rapidly growing mass mobility needs, and curb personal motor vehicle activity and its impacts at low cost. Indian cities rely predominantly on buses for public transport, and are likely to continue to do so for years. However, the public bus transit service is inadequate, and unaffordable for the urban poor. The paper explores the factors that contribute to and affect efforts to improve this situation, based on an analysis of the financial and operational performance of the public bus transit service in the four metropolitan centres and four secondary cities during the 1990s. Overall, there were persistent losses, owing to increasing input costs and declining productivity. The losses occurred despite rapidly increasing fares, and ridership declined. The situation, and the ability to address it, is worse in the secondary cities than the metropolitan centres. We suggest a disaggregated approach based on the needs and motivations of different groups in relation to public transit, along with improved operating conditions and policies to internalize costs of personal motor vehicle use, to address the challenge of providing financially viable and affordable public bus transit service.     

Reducing transit fleet emissions through vehicle retrofits,replacements, and usage changes over multiple time periods

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO₂ equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.     

Scheduling considerations for a branching transit route

This paper addresses the impacts of different scheduling alternatives for a branching transit route. It examines different schedule alternatives that might be used to optimize the route performance in terms of the passenger traveling time distributed among branch passengers and trunk‐line passengers. The schedule alternatives considered include transit vehicle allocation to different branches, offset shifting across vehicles on different branches, and vehicle holding (slack time) in the transit vehicle schedule. With these variables, several vehicle schedules are devised and examined based on a wide variety of possible passenger boarding scenarios using deterministic service models. Test outcomes provide general conclusions about the performance of the strategies. Vehicle assignment leading to even headways among branches is generally preferred for the case of low passenger demand. However, when passenger demand is high, or the differences between the passenger demands on branches are significant, unequal vehicle assignment will be helpful to improve the overall route performance. Holding, as a proactive strategy in scheduling, has the potential to be embedded into the schedule as a type of slack time, but needs further evidence and study to determine the full set of conditions where it may be beneficial. Offset shifting does not show sufficient evidence to be an efficient strategy to improve route performance in the case of low or high passenger demand.     

Demand responsive transit and the integration of D/R systems with traditional transit

This paper provides a background of the development of demand-responsive transit in small communities in the U.S.A. It also backgrounds traditional transit in metropolitan areas of the United States and outlines its deficiencies in terms of today's urban sprawl and in terms of today's society in metropolitan areas. Urban sprawl has developed city-like areas around big cities, but with lower population densities. Highways and roads were built. Cars were mass-produced. These new populations have never had an alternative to the private car. Today's society includes an ever-increasing number of senior citizens and handicapped persons. Senior citizens find it difficult to get to fixed-route bus stops; handicapped persons have difficulty in boarding regular buses and wheelchair persons cannot even get on board. Particular emphasis is placed upon the examination of the development of demand-responsive transit in metropolitan Rochester, U.S.A. and a Demonstration Project, sponsored by the Urban Mass Transportation Administration of the United States Department of Transportation. The paper also examines and makes reference to the results of integrated transit in Regina, Saskatchewan, Canada. The Demonstration Project has several key objectives, the principal one of which is the integration of demand-responsive transit with the fixed-route element of traditional transit. Other important objectives of the demonstration are the balancing of peak and off-peak service so as to improve the overall utilization of resources, increase transit coverage, regular (not special) service for the elderly and the handicapped, the utilization of a computer in dispatching, digital communications, and marketing and promotional techniques.     

The impact of automated transit,pedestrian, and bicycling facilities on urban travel patterns

This article reports on an integrated modeling exercise, conducted on behalf of the US Federal Highway Administration, on the potential for frequent automated transit shuttles (‘community transit’), in conjunction with improvements to the walking and cycling environment, to overcome the last-mile problem of regional rail transit and thereby divert travelers away from car use. A set of interlocking investigations was undertaken, including development of urban visualizations, distribution of a home-based survey supporting a stated-preference model of mode choice, development of an agent-based model, and alignment of the mode-choice and agent-based models. The investigations were designed to produce best-case estimates of the impact of community transit and ancillary improvements in reducing car use. The models in combination suggested significant potential to divert drivers, especially in areas that were relatively transit-poor to begin with.     

Refining light rapid transit typology: a UK perspective

Abstract

Recent developments in the light rapid transit sector have introduced transit modes that are attempting to imitate the performance of others, e.g. buses with tram-like characteristics. The boundaries between existing definitions of what is a bus, tram or train are becoming blurred. For transport studies and practice this requires a review of how we define modes. This is not just a matter of semantics, but has safety and competition regulation implications for system operators. This paper proposes a structure to produce rail- and bus-based transit mode definitions and typology that are appropriate for modern use. A decision tree is used to classify and define the transit modes as guided-bus, trolley-bus, light rail and tram-train and is provided with example systems. The paper provides a robust definitional framework that allows transit system promoters, operators and other interested parties to have a consistent basis of reference when specifying and comparing rapid transit systems.     

Passenger-demands-oriented train scheduling for an urban rail transit network

This paper considers the train scheduling problem for an urban rail transit network. We propose an event-driven model that involves three types of events, i.e., departure events, arrival events, and passenger arrival rates change events. The routing of the arriving passengers at transfer stations is also included in the train scheduling model. Moreover, the passenger transfer behavior (i.e., walking times and transfer times of passengers) is also taken into account in the model formulation. The resulting optimization problem is a real-valued nonlinear nonconvex problem. Nonlinear programming approaches (e.g., sequential quadratic programming) and evolutionary algorithms (e.g., genetic algorithms) can be used to solve this train scheduling problem. The effectiveness of the event-driven model is evaluated through a case study.