A dynamic route choice model for public transport networks with boarding queues

Abstract

The concepts of optimal strategy and hyperpath were born within the framework of static frequency-based public transport assignment, where it is assumed that travel times and frequencies do not change over time and no overcrowding occurs. However, the formation of queues at public transport stops can prevent passengers from boarding the first vehicle approaching and can thus lead to additional delays in their trip. Assuming that passengers know from previous experience that for certain stops/lines they will have to wait for the arrival of the 2nd, 3rd, …, k-th vehicle, they may alter their route choices, thus resulting in a different assignment of flows across the network. The aim of this paper is to investigate route choice behaviour changes as a result of the formation and dispersion of queues at stops within the framework of optimal travel strategies. A new model is developed, based on modifications of existing algorithms.     

New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas

The percentage of the population being served by a transit system in a metropolitan region is a key system performance measure but depends heavily on the definition of service area. Observing existing service areas can help identify transit system gaps and redundancies. In the public transit industry, buffers at 400 m (0.25 miles) around bus stops and 800 m (0.5 miles) around rail stations are commonly used to identify the area from which most transit users will access the system by foot. This study uses detailed OD survey information to generate service areas that define walking catchment areas around transit services in Montreal, Canada. The 85th percentile walking distance to bus transit service is found to be around 524 m for home-based trip origins, 1,259 m for home-based commuter rail trip origins. Yet these values are found to vary based on our analysis using two statistical models. Walking distances vary based on route and trip qualities (such as type of transit service, transfers and wait time), as well as personal, household, and neighbourhood characteristics. Accordingly, service areas around transit stations should vary based on the service offered and attributes of the people and places served. The generated service areas derived from the generalized statistical model are then used to identify gaps and redundancies at the system and route level using Montreal region as an example. This study can be of benefit to transport engineers and planners trying to maximize transit service coverage in a region while avoiding oversupply of service.     

Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai

ABSTRACT

Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3?min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.     

A zonal inference model based on observed smart-card transactions for Santiago de Chile

The collection of origin–destination data for a city is an important but often costly task. This way, there is a need to develop more efficient and inexpensive methods of collecting information about citizens’ travel patterns. In this line, this paper presents a generic methodology that allows to infer the origin and destination zones for an observed trip between two public transport stops (i.e., bus stops or metro stations) using socio-economic, land use, and network information. The proposed zonal inference model follows a disaggregated Logit approach including size variables. The model enables the estimation of a zonal origin–destination matrix for a city, if trip information passively collected by a smart-card payment system is available (in form of a stop-to-stop matrix). The methodology is applied to the Santiago de Chile’s morning peak period, with the purpose of serving as input for a public transport planning computational tool. To estimate the model, information was gathered from different sources and processed into a unified framework; data included a survey conducted at public transport stops, land use information, and a stop-to-stop trip matrix. Additionally, a zonal system with 1176 zones was constructed for the city, including the definition of its access links and associated distances. Our results shows that, ceteris paribus, zones with high numbers of housing units have higher probabilities of being the origin of a morning peak trip. Likewise, health facilities, educational, residential, commercial, and offices centres have significant attraction powers during this period. In this sense, our model manages to capture the expected effects of land use on trip generation and attraction. This study has numerous policy implications, as the information obtained can be used to predict the impacts of changes in the public transport network (such as extending routes, relocating their stops, designing new routes or changing the fare structure). Further research is needed to improve the zonal inference formulation and origin–destination matrix estimation, mainly by including better cost measures, and dealing with survey and data limitations.     

A systematic analysis of multimodal transport systems with road space distribution and responsive bus service

A smart design of transport systems involves efficient use and allocation of the limited urban road capacity in the multimodal environment. This paper intends to understand the system-wide effect of dividing the road space to the private and public transport modes and how the public transport service provider responds to the space changes. To this end, the bimodal dynamic user equilibrium is formulated for separated road space. The Macroscopic Fundamental Diagram (MFD) model is employed to depict the dynamics of the automobile traffic for its state-dependent feature, its inclusion of hypercongestion, and its advantage of capturing network topology. The delay of a bus trip depends on the running speed which is in turn affected by bus lane capacity and ridership. Within the proposed bimodal framework, the steady-state equilibrium traffic characteristics and the optimal bus fare and service frequency are analytically derived. The counter-intuitive properties of traffic condition, modal split, and behavior of bus operator in the hypercongestion are identified. To understand the interaction between the transport authority (for system benefit maximization) and the bus operator (for its own benefit maximization), we examine how the bus operator responds to space changes and how the system benefit is influenced with the road space allocation. With responsive bus service, the condition, under which expanding bus lane capacity is beneficial to the system as a whole, has been analytically established. Then the model is applied to the dynamic framework where the space allocation changes with varying demand and demand-responsive bus service. We compare the optimal bus services under different economic objectives, evaluate the system performance of the bimodal network, and explore the dynamic space allocation strategy for the sake of social welfare maximization.     

The travel and environmental implications of shared autonomous vehicles,using agent-based model scenarios

Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.     

Bus congestion, optimal infrastructure investment and the choice of a fare collection system in dedicated bus corridors

Microeconomic optimisation of scheduled public transport operations has traditionally focused on finding optimal values for the frequency of service, capacity of vehicles, number of lines and distance between stops. In addition, however, there exist other elements in the system that present a trade-off between the interests of users and operators that have not received attention in the literature, such as the optimal selection of a fare payment system and a designed running speed (i.e., the cruising speed that buses maintain in between two consecutive stops). Alternative fare payment methods (e.g., on-board and off-board, payment by cash, magnetic strip or smart card) have different boarding times and capital costs, with the more efficient systems such as a contactless smart card imposing higher amounts of capital investment. Based on empirical data from several Bus Rapid Transit systems around the world, we also find that there is a positive relationship between infrastructure cost per kilometre and commercial speed (including stops), achieved by the buses, which we further postulate as a linear relationship between infrastructure investment and running speed. Given this context, we develop a microeconomic model for the operation of a bus corridor that minimises total cost (users and operator) and has five decision variables: frequency, capacity of vehicles, station spacing, fare payment system and running speed, thus extending the traditional framework. Congestion, induced by bus frequency, plays an important role in the design of the system, as queues develop behind high demand bus stops when the frequency is high. We show that (i) an off-board fare payment system is the most cost effective in the majority of circumstances; (ii) bus congestion results in decreased frequency while fare and bus capacity increase, and (iii) the optimal running speed grows with the logarithm of demand.     

The simple,compelling case for PRT

When compared with existing urban modes, Personal Rapid Transit (PRT) offers compelling advantages in every important respect. The desirable attributes of any urban mobility mode are well documented, such as minimal travel time, safety, comfort, low cost, and minimal impacts. These attributes are used to define a hypothetical, ideal urban mode. The ideal mode would possess characteristics such as no waiting, no stops, from anywhere to anywhere service, risk-free, non-polluting, and accessible to everyone at any time. Although not attainable in the real world, the ideal provides a model and benchmark for design much as the Carnot thermodynamic cycle guides the development of internal combustion engines, or ideal gases and perfect fluids are postulated in physics. A matrix format is used to present the characteristics of various modes against the desirable attributes of any mode. Modes presented are the ideal, walking, bicycles, motorcycles, automobiles, taxis, buses, rail transit, Automated Guideway Transit (AGT), and PRT. In all respects, PRT is shown to approach the ideal much more closely than competing modes.     

Identifying preferences for public transport investments under a constrained budget

As urban areas face increasing demands for new transport infrastructure to promote a sustainable future with an increasing reality of constrained government budgets, the debate on whether we should focus on rail or bus-based investments continues unabated in many jurisdictions. Associated with the debate is an emotional (or ideological) bias by communities in favour of one mode, especially rail, which carries much sway at the political level as if there is no budget constraint. This paper presents a stated choice experiment to investigate this context as two unlabelled options described by 20 potential drivers of community preferences for improved public transport, where each choice scenario is conditioned on an estimated construction cost and a total annual transport infrastructure budget for the relevant geographical jurisdiction. This is followed by a labelling of each alternative to reveal whether the option is bus rapid transit (BRT) or light rail (LRT) and to establish whether this additional information influences preference revision. Data is collected in all eight capital cities of Australia in mid 2014. Mixed logit models with heteroscedastic conditioning in terms of the cost of the project infrastructure and whether the alternative is labelled BRT or LRT, provide new evidence on the nature and extent of community modal bias in a budget-constrained choice setting. The conclusions are twofold. On the one hand, if a fully compensatory choice rule is assumed (as is common in all previous modal comparison studies), LRT is predominantly preferred over BRT despite budgetary constraints, similarities in quality of service attributes and the opportunity to choose a greater network coverage for a given construction cost. However, when we allow for attribute non-attendance (a semi-compensatory choice rule), the modal bias is no longer a significant driver of preferences.     

Integrated imputation of activity-travel diaries incorporating the measurement of uncertainty

Procedures to transform GPS tracks into activity-travel diaries have been increasingly addressed due to their potential benefit to replace traditional methods used in travel surveys. Existing approaches for data annotation however are not sufficiently accurate, which normally involves a prompted recall survey for data validation. Imputation algorithms for transportation mode detection seem to be largely dependent on speed-related features, which may blur the quality of classification results, especially with transportation modes having similar speeds. Therefore, in this paper we propose an enhanced integrated imputation approach by incorporating the critical indicators related to trip patterns, reflecting the effects of uncertain travel environments, including bus stops and speed percentiles. A two-step procedure which embeds a segmentation model and a transportation mode inference model is designed and examined based on purified prompted recall data collected in a large-scale travel survey. Results show the superior performance of the proposed approach, where the overall accuracy at trip level reaches 93.2% and 88.1% for training and surveyed data, respectively.     

An intersection control strategy for a short‐headway P.R.T. network

Much PRT development and research is currently being undertaken assuming quasi‐synchronous longitudinal control of guideway vehicles. This method of control has the characteristic that intersection performance has a substantial influence on the efficiency of trip demand processing. An algorithm for the control of a PRT intersection is discussed here, which would appear to have significant advantages over all other known existing stratagems. The stratagem is not only efficient but its flexibility facilitates tailoring to diverse local conditions; furthermore, the algorithm does not require intractable computations or excessive computer memory requirements. The algorithm is described and simulation results are presented. A comparative study is also made between this algorithm and its fore‐runner.     

A model of stoppings on urban bus routes

The amount of time required to pick up and discharge passengers is an important issue in the planning and modeling of urban bus systems. Several past studies have employed models of this component of bus travel time which are based, in part, on a model of the number of stoppings the bus makes to pick up or discharge passengers. Most past versions of this model have assumed that expected demand does not vary from stop to stop or from trip to trip, but that the number of passengers demanding service at any given stop during any given trip follows a Poisson distribution. An alternative model is derived, based on the assumption that expected demand varies among stops and times of day but is fixed from day to day at any given stop and time of day. Boarding and alighting survey data are used to verify that the “average-demand” Poisson model consistently overestimates the number of stoppings and to calibrate an approximate version of the alternative model. A stop-spacing optimization model previously developed by Kikuchi and Vuchic is reevaluated using the alternative stopping model in place of the average demand model used in the original version. The results are found to be considerably different, thus indicating that transit route optimization models are sensitive to the way in which stopping processes are modeled.     

Energy use in personal rapid transit building and running personal rapid transit in Sweden

Travel by a Personal Rapid Transit (PRT) system may be much more energy efficient than travel by conventional road transport. The difference could be so large that the energy invested in the PRT infrastructure may be equivalent to the fuel that is saved by previous car and bus riders in less than five years. We analyzed the propulsion energy requirements of a PRT system and made a first-order calculation of the energy cost of the infrastructure and maintenance. Operation of the PRT requires only half the energy required by buses and a quarter of the energy used by passenger cars per passenger kilometer. The energy used to build the PRT infrastructure in a city may be recovered in five years if 10% of the car drivers switch to the PRT.     

Assessing systematic sources of variation in public transport elasticities: Some comparative warnings

There is an extensive and continually growing body of empirical evidence on the sensitivity of potential and actual users of public transport to fare and service levels. The sources of the evidence are disparate in terms of methods, data collection strategy, data paradigms, trip purpose, location, time period, and attribute definition. In this paper, we draw on a data set we have been compiling since 2003 that contains over 1100 elasticity items associated with prices and services of public transport, and car modes. The focus herein is on direct elasticities associated with public transport choice and demand, and the systematic sources of influence on the variations in the mean estimates for fares, in-vehicle time, and headway obtained from 319 studies. The major influences on variations in mean estimates of public transport elasticities are the time of day (peak, all day vs. off-peak), the data paradigm (especially combined SP/RP vs. revealed preference (RP)), whether an average fare or class of tickets is included, the unit of analysis (trips vs. vkm), specific trip purposes, country, and specific-mode (i.e., bus and train) in contrast to the generic class of public transport.     

It is time to give the “true PRT” concept a fair test

True Personal Rapid Transit (PRT), in concept and technology, is considerably advanced over PRT as proposed in the 1960s and 1970s. It is one of the few advanced transit concepts—perhaps the only one—that offers a prospect of overcoming the continued serious deficiencies of conventional transit systems. Past criticisms of PRT were often invalid, but even those questions that deserved to be taken seriously then no longer justify a postponement of serious PRT testing. Conventional transit, in spite of over two decades of considerable governmental support, is proving unable to satisfy major transportation needs of American urban areas, which are increasingly dispersed and multi-centered. There is no indication that conventional transit can overcome sufficiently its performance and economic deficiencies. If PRT is not given a chance to be tested, local public officials will continue to be faced with too limited options as between auto-dominated transportation services and high subsidies for inadequate transit solutions.     

Towards modal choice decision-making models: A case study of Lagos state transport corporation bus riders

Identification of the socioeconomic factors which affect the demand for buses, and the analysis of the use of the other transport modes by bus users are the two main objectives of this article. Work and school trips are highlighted as being very important trip purposes in Lagos metropolis by the multiple discriminant analysis model. It identifies mode of transport, distance, travel time, reliability, and the number of stops as significant mode choice variables. Multiple linear regression models for work and school trips identify mode of transport, transfort fare, travel time, annual income, and crew behaviour as significant variables in the choice of transport mode. These findings support the two alternative hypotheses of the study that the choice of bus is related to the individual perception of the quality of service of the different modes and that socioeconomic characteristics of the riders influence the patronage of buses. The attention of policy makers for the 22 transport corporations that operate inter-and intra-urban services in all the 21 states and the federal capital of Abuja in Nigeria is drawn to the importance of these variables for decisions.     

Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model

Although smart-card data were expected to substitute for conventional travel surveys, the reality is that only a few automatic fare collection (AFC) systems can recognize an individual passenger's origin, transfer, and destination stops (or stations). The Seoul metropolitan area is equipped with a system wherein a passenger's entire trajectory can be tracked. Despite this great advantage, the use of smart-card data has a critical limitation wherein the purpose behind a trip is unknown. The present study proposed a rigorous methodology to impute the sequence of activities for each trip chain using a continuous hidden Markov model (CHMM), which belongs to the category of unsupervised machine-learning technologies. Coupled with the spatial and temporal information on trip chains from smart-card data, land-use characteristics were used to train a CHMM. Unlike supervised models that have been mobilized to impute the trip purpose to GPS data, A CHMM does not require an extra survey, such as the prompted-recall survey, in order to obtain labeled data for training. The estimated result of the proposed model yielded plausible activity patterns that are intuitively accountable and consistent with observed activity patterns.     

Using non-real-time Automatic Vehicle Location data to improve bus services

Although real-time Automatic Vehicle Location (AVL) data is being utilised successfully in the UK, little notice has been given to the benefits of historical (non-real-time) AVL data. This paper illustrates how historical AVL data can be used to identify segments of a bus route which would benefit most from bus priority measures and to improve scheduling by highlighting locations at which the greatest deviation from schedule occurs. A new methodology which uses historical AVL data and on-bus passenger counts to calculate the passenger arrival rate at stops along a bus route has been used to estimate annual patronage and the speed of buses as they move between stops. Estimating the patronage at stops using AVL data is more cost-effective than conventional methods (such as surveys at stops which require much more manpower) but retains the benefits of accuracy and stop-specific estimates of annual patronage. The passenger arrival rate can then be used to calculate how long buses spend at stops. If the time buses spend at stops is removed from the total time it takes the bus to traverse a link, the remaining amount of time can be assumed to be the time the bus spends moving and hence the moving speed of the bus can be obtained. It was found that estimation of patronage and the speed of buses as they move between stops using AVL data produced results which were comparable with those obtained by other methods. However the main point to note is that this new method of estimating patronage has the potential to provide a larger and superior data set than is otherwise available, at very low cost.     

Exploring public transport usage trends in an ageing population

An ageing population remains one of the most significant challenges for Western society in the 21st century. Whilst public transport use has attractive sustainability features for older generations there is mixed evidence with regard to trends in travel and public transport use in ageing societies. This paper explores public transport trip rates amongst older age groups using travel survey evidence collected from a household travel survey in Melbourne, Australia for the period 1994 to 1999. A particular aim of the research was to establish trends in trip rates so as to explore the impact of the ageing Baby Boomer generation on travel by public transport. The results suggested that compared to those aged below 60, those aged over 60 years demonstrated 30% lower trip making overall and 16% lower public transport trip rates. Longitudinal trends in trip rates showed those aged over 60 had a very small decline in trip rates by public transport (−0.004 average daily trips per annum) but increasing rates for car trips. A further analysis showed a small but significant increase in longitudinal trip rates of public transport use amongst Baby Boomers (0.004 daily trips p.a., p < .05) while car usage for Baby Boomers was steady. The implication of these findings is that trends in the existing over 60s population are not necessarily going to flow through to behaviour patterns in the Baby Boomer generations. The Baby Boomer age group showed longitudinal trends in travel behaviour which contrasted with those of the existing over 60s generation notably with a trend towards increased public transport usage.