A behavioural comparison of route choice on metro networks: Time,transfers, crowding,topology and socio-demographics

Understanding travellers’ behaviour is key element in transportation planning. This article presents a route choice model for metro networks that considers different time components as well as variables related to the transferring experience, train crowding, network topology and socio-demographic characteristics. The route choice model is applied to the London Underground and Santiago Metro networks, to make a comparison of the decision making process of the users on both cities. As all the variables are statistically significant, it is possible to affirm that public transport users take into account a wide variety of elements when choosing routes. While in London the travellers prefer to spend time walking, in Santiago is preferable to spend time waiting. Santiago Metro users are more willing to travel in crowded trains than London Underground users. Both user groups have a similar dispreference to transfers after controlling for the time spent on transfer, but different attitudes to ascending and descending transfers. Topological factors presented on a distorted Metro map are more important than actual topology to passengers’ route choice decisions.     

Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models

A growing base of research adopts direct demand models to reveal associations between transit ridership and influence factors in recent years. This study is designed to investigate the factors affecting rail transit ridership at both station level and station-to-station level by adopting multiple regression model and multiplicative model respectively, specifically using an implemented Metro system in Nanjing, China, where Metro implementation is on the rise. Independent variables include factors measuring land-use mix, intermodal connection, station context, and travel impedance. Multiple regression model proves 11 variables are significantly associated with Metro ridership at station level: population, employment, business/office floor area, CBD dummy variable, number of major educational sites, entertainment venues and shopping centers, road length, feeder bus lines, bicycle park-and-ride (P&R) spaces, and transfer dummy variable. Results from multiplicative model indicate that factors influencing Metro station ridership may also influence Metro station-to-station ridership, varied by both trip ends (origin/destination) and time of day. In comparison with previous case studies, CBD dummy variable and bicycle P&R are statistically significant to explain Metro ridership in Nanjing. In addition, Metro travel impedance variables have significant influence on station-to-station ridership, representing the basic time-decay relationship in travel distribution. Potential implications of the model results include estimating Metro ridership at station level and station-to-station level by considering the significant variables, recognizing the necessity to establish a cooperative multi-modal transit system, and identifying opportunities for transit-oriented development.     

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

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

     

Should I stay or should I go? Investigating route switching behavior from revealed preferences data

A large variety of factors influence the route choice decisions of road users, and modelers consider these factors within the perceived utility that road users are assumed to maximize. However, this perceived utility may be different even for the same origin–destination pair and this leads road users to choose different routes for different trips. In this study, we focus on this particular phenomenon of route switching behavior by estimating discrete choice models with the aim of understanding the key factors at its foundation. The estimated route choice models account for route characteristics, socio-economic information, activity based data, inertial mechanism and learning effects, and they are applied to revealed preference data consisting of 677 actual day by day route choices (referred to 77 road users) collected by GPS in Cagliari (Italy). Route switching models were estimated with both fixed and random coefficient models. The model estimation results show that the variables referred to habit and learning have an important relevance on explaining the route switching phenomenon. Specifically, the higher is the travel habit, the less is the propensity of the road users to switch their route. Moreover, the learning effect shows that the accumulation of past experiences has more influence on the choice than the most recent ones.

     

An analysis of Metro ridership at the station-to-station level in Seoul

While most aggregate studies of transit ridership are conducted at either the stop or the route level, the present study focused on factors affecting Metro ridership in the Seoul metropolitan area at the station-to-station level. The station-to-station analysis made it possible to distinguish the effect of origin factors on Metro ridership from that of destination factors and to cut down the errors caused by the aggregation of travel impedance-related variables. After adopting two types of direct-demand patronage forecasting models, the multiplicative model and the Poisson regression model, the former was found to be superior to the latter because it clearly identified the negative influences of competing modes on Metro ridership. Such results are rarely found with aggregate level analyses. Moreover, the importance of built environment in explaining Metro demand was confirmed by separating built environment variables for origin and destination stations and by differentiating ridership by the time of day. For morning peak hours, the population-related variables of the origin stations played a key role in accounting for Metro ridership, while employment-related variables prevailed in destination stations. In evening peak hours, both employment- and population-related variables were significant in accounting for the Metro ridership at the destination station. This showed that a significant number of people in the Seoul metropolitan area appear to take various non-home-based trips after work, which is consistent with the results from direct household travel surveys.     

Planning guidelines for metro–bus interchanges by means of a pedestrian microsimulation model

The big paradigm for cities nowadays is to study the movement of pedestrians at the interface between metro and bus systems – metro–bus interchanges. When these interchanges are not well designed, walking is inefficient and can be unsafe for pedestrians. This paper analyses, by means of a pedestrian microsimulation model, metro–bus interchange spaces in order to propose planning guidelines for the city of Santiago de Chile. Specific objectives are (1) to identify the variables that provide efficiency and safety in those spaces; (2) to simulate different scenarios using the pedestrian simulation model LEGION; (3) to propose planning and design guidelines for pedestrian spaces at metro–bus interchanges; and (4) to contrast the recommendations in the recently opened terminal station on Line 1 of Metro de Santiago: Los Dominicos Station.     

On the effect of operational service attributes on transit satisfaction

Public transport (PT) providers aim to offer services that meet users’ satisfaction, and for this, they can control some operational service attributes such as frequency, speed, crowdedness and reliability. Understanding how these objective attributes affect user satisfaction is essential to improve it cost-effectively, but these associations have not been examined enough in the PT literature. This study aims to unveil how key transit operational variables actually experienced by users affect their satisfaction. We analysed data derived from a multiannual consumer satisfaction survey for the Santiago de Chile Metro system; between January 2013 and June 2016 (n?=?41,993), where approximately 1000 questionnaires were completed each month. We also gained access to a set of operational variables managed by Metro for the same period, including more than 1.4 million records. With this unique dataset, we first developed a structural equation model (SEM) with users’ perceived attributes, finding that safety, ease of boarding, response to critical incidents (CI), the number and type of CI endured, and information, were the variables that mostly affected satisfaction. We also examined heterogeneity in transit satisfaction with SEM-MIMIC models, by characterising the user population through their trip and socioeconomic characteristics, finding a striking result: that as users age they are more satisfied with the system. Next, we assessed whether including operational service attributes, such as crowding levels, frequency, commercial speed and CI, added predictive power to the proposed model. We found that the number of CI, speed, frequency and crowdedness, plus their variability (measured through the coefficient of variation), affected transit satisfaction at significant levels. Including these objective service attributes provided more explanatory power to the SEM-MIMIC transit satisfaction models. Policy recommendations for improving satisfaction, derived from our results, are: to implement an automatic control system for the number of passengers on Metro platforms (as safety and ease of boarding are critical issues for passengers); and to deploy a comprehensive tactical plan to address CI: determine which happen more often, take actions to minimise them and provide better responsive actions.

     

Designing a route planner to facilitate and promote cycling in Metro Vancouver,Canada

With increasing fuel costs, greater awareness of greenhouse gas emissions and increasing obesity levels, cycling is promoted as a health promoting and sustainable transport mode. We developed a cycling route planner (http://cyclevancouver.ubc.ca) for Metro Vancouver, British Columbia, Canada, to facilitate cycling amongst the general public and to facilitate new route location by transportation planners. The geographical information system-based planner incorporates variables that influence choices to travel by bicycle (e.g., distance, elevation gain, safety, route features, air pollution and links to transit) in selecting the preferred routing. Using a familiar and user-friendly Google Maps interface, the planner allows individuals to seek optimized cycling routes throughout the region based on their own preferences. In addition to the incorporation of multiple user preferences in route selection, the planner is unique amongst cycling route planners in its use of topology to minimize data storage redundancy, its reliance on node/vertex index tables to increase efficiency of the route selection process, and the use of web services and asynchronous technologies for quick data delivery. Use of this tool can help promote bicycle travel as a form of active transportation and help lower greenhouse gas carbon dioxide (CO₂) and air pollutant emissions by reducing car trips.     

Where do cyclists ride? A route choice model developed with revealed preference GPS data

To better understand bicyclists’ preferences for facility types, GPS units were used to observe the behavior of 164 cyclists in Portland, Oregon, USA for several days each. Trip purpose and several other trip-level variables recorded by the cyclists, and the resulting trips were coded to a highly detailed bicycle network. The authors used the 1449 non-exercise, utilitarian trips to estimate a bicycle route choice model. The model used a choice set generation algorithm based on multiple permutations of path attributes and was formulated to account for overlapping route alternatives. The findings suggest that cyclists are sensitive to the effects of distance, turn frequency, slope, intersection control (e.g. presence or absence of traffic signals), and traffic volumes. In addition, cyclists appear to place relatively high value on off-street bike paths, enhanced neighborhood bikeways with traffic calming features (aka “bicycle boulevards”), and bridge facilities. Bike lanes more or less exactly offset the negative effects of adjacent traffic, but were no more or less attractive than a basic low traffic volume street. Finally, route preferences differ between commute and other utilitarian trips; cyclists were more sensitive to distance and less sensitive to other infrastructure characteristics for commute trips.     

Modelling the potential for cycling in access trips to bus,train and metro in Rio de Janeiro

Most studies that address the integration of cycling and public transport (PT) focus on developed countries and deal with multi-modal bicycle-train trips. Little is known about the integration of cycling and other main modes such as bus and metro, especially in developing countries, where entirely different socio-economic and trip making conditions prevail. The aim of this study is to model the propensity of current PT users to shift to the bicycle in access trips to bus stops, train and metro stations in Rio de Janeiro, Brazil. Interviews were conducted to collect data on the socio-economic characteristics of the interviewee, trip and spatial characteristics and self-reported barriers and motivators for bicycle use. Two binary logit models were estimated to predict the main factors affecting the propensity to use a bicycle as feeder mode to PT. The results show that socio-economic characteristics as well as barriers and motivators are important factors to explain propensity for bike and ride. The barriers’ model reveals that personal constraints, living too close to the PT boarding point, current parking conditions and public safety play a role. For the motivators’ model, changing home location, owning a bicycle, implementation of cycle ways and improvement in parking conditions are explanatory. Policy recommendations are formulated to increase bicycle ownership and improve cycling infrastructure.     

Optimization of a feeder-bus route design by using a multiobjective programming approach

This paper presents a feeder-bus route design model, capable of minimizing route length, minimizing maximum route travel time of planned routes, and maximizing service coverage for trip generation. The proposed model considers constraints of route connectivity, subtour prevention, travel time upper bound of a route, relationships between route layout and service coverage, and value ranges of decision variables. Parameter uncertainties are dealt with using fuzzy numbers, and the model is developed as a multiobjective programming problem. A case study of a metro station in Taichung City, Taiwan is then conducted. Next, the programming problem in the case study is solved, based on the technique for order preference by similarity to ideal solution approach to obtain the compromise route design. Results of the case study confirm that the routes of the proposed model perform better than existing routes in terms of network length and service coverage. Additionally, increasing the number of feeder-bus routes decreases maximum route travel time, increases service coverage, and increases network length. To our knowledge, the proposed model is the first bus route design model in the literature to consider simultaneously various stakeholder needs and support for bus route planners in developing alternatives for further evaluation efficiently and systematically.     

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.     

Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice

In the current paper, we propose the use of a multivariate skew-normal (MSN) distribution function for the latent psychological constructs within the context of an integrated choice and latent variable (ICLV) model system. The multivariate skew-normal (MSN) distribution that we use is tractable, parsimonious in parameters that regulate the distribution and its skewness, and includes the normal distribution as a special interior point case (this allows for testing with the traditional ICLV model). Our procedure to accommodate non-normality in the psychological constructs exploits the latent factor structure of the ICLV model, and is a flexible, yet very efficient approach (through dimension-reduction) to accommodate a multivariate non-normal structure across all indicator and outcome variables in a multivariate system through the specification of a much lower-dimensional multivariate skew-normal distribution for the structural errors. Taste variations (i.e., heterogeneity in sensitivity to response variables) can also be introduced efficiently and in a non-normal fashion through interactions of explanatory variables with the latent variables. The resulting model we develop is suitable for estimation using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The proposed model is applied to model bicyclists’ route choice behavior using a web-based survey of Texas bicyclists. The results reveal evidence for non-normality in the latent constructs. From a substantive point of view, the results suggest that the most unattractive features of a bicycle route are long travel times (for commuters), heavy motorized traffic volume, absence of a continuous bicycle facility, and high parking occupancy rates and long lengths of parking zones along the route.     

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.     

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.     

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.     

Design of public transit network in urban area with elastic demand

This research focuses on an efficient design of transit network in urban areas. The system developed is used to create, analyze and optimize routes and frequencies of transit system in the network level. The analysis is based on elastic demand, so the shift of demand between modes in network due to different service level is of prime consideration. The developed system creates all feasible routes connecting all pairs of terminals in the network. Out of this vast pool of routes, a set of optimal routes is generated for a certain predetermined number that maintains connectivity of significant demand. Based on these generated routes, the system fulfils transportation demand by assigning demand that considers path and route choices for non-transit users and transit users. Together with the assignment of demand, transit frequencies are optimized and the related fleet-size is calculated. Having an optimal setting of solution, the system is continued by reconnecting the routes to find some other better solutions in the periphery of the optimal setting. A set of mathematical programming modules is developed. Real data from Sioux Falls city network is used to evaluate the performance of the model and compare with other heuristic methods.     

Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging.     

Motivators and deterrents of bicycling: comparing influences on decisions to ride

In a survey of 1,402 current and potential cyclists in Metro Vancouver, 73 motivators and deterrents of cycling were evaluated. The top motivators, consistent among regular, frequent, occasional and potential cyclists, were: routes away from traffic noise and pollution; routes with beautiful scenery; and paths separated from traffic. In factor analysis, the 73 survey items were grouped into 15 factors. The following factors had the most influence on likelihood of cycling: safety; ease of cycling; weather conditions; route conditions; and interactions with motor vehicles. These results indicate the importance of the location and design of bicycle routes to promote cycling.     

Evaluation of a public transportation level of service concept

The level of service of a bus line is evaluated by its operational characteristics, particularly by the ratio between average bus travel time on a given route and the average passenger car travel time on the shortest distance between the origin and the destination of the bus in question. It is shown that the level-of-service measure may be predicted by such independent variables as route length, average distance between bus stations, number of signalized and unsignalized intersections, and the ratio between such intersections. It is hypothesized that use of other independent variables such as boarding and alighting passengers, or volume to capacity ratio on the route concerned, could improve the predictive power of the suggested models. Further research is recommended on the effect of these latter variables and other operational variables which might influence bus level of service, and also on the comparison between direct bus lines and lines which use transfer points.