A random utility maximization (RUM) based measure of accessibility to transit: Accurate capturing of the first-mile issue in urban transit
Introduction
The public transport system is considered one of the key agents in achieving the objectives of sustainability in urban transportation, and so investment in public transit is widely touted by planners and policymakers (Albacete et al. 2017). However, investments in transit infrastructure alone cannot increase the chance of transit being a dominant travel mode. Many other factors affect the choice of using transit by urban dwellers. Among these, accessibility to transit is one of the most crucial ones (Al Mamun and Lownes 2011, Foda and Osman 2010). A transit stop/station is the first point of access for the individuals to the transit service. This access to the first transit stop is well known as the “first-mile” problem. Measuring access to transit is a necessary measure of transit accessibility.
Accessibility refers to the ease of getting service, with minimum time, cost and inconvenience. In our context, accessibility to transit is the ease of getting into a transit service (Hansen 1959). In general, three types of accessibility measures are commonly found in the literature:
- •
Count-based opportunities measures,
- •
Gravity-based measures and
- •
Utility-based measures.
In the count-based measures, a count of available opportunities near the origin (define by a range of areas) of the trip is used as a measure of accessibility to the opportunities. This is a straightforward and easy measure of accessibility. For access to transit measurement, the count-based measure of accessibility is the total number of transit stops/stations from the origin and within a specific distance range. For example, the number of bus stops within 30 min walking time can be a measurement of access to bus services. The first issue related to this measurement is that the threshold/range of walk time is needed to be defined, often arbitrarily. Besides, the implicit assumption of this count-based measurement is that all transit stops/stations within the range/threshold are equally attractive. Thus, the differences in travel impedances among the alternative locations within the range are overlooked in the count-based measures (El-Geneidy et al. 2016, Geurs and van Wee 2004, Vickerman, 1974).
A gravity-based measurement of accessibility discounts the opportunities that are in farther away locations from the origin by using a travel impedance measure. Typically, the denominator of an aggregate trip distribution gravity model is used to define the travel impedance measure for this approach (Niemeier 1997, Handy and Niemeier 1997). Gravity-based measures overcome the limitation of count-based measures by accommodating travel impedance and differentiating opportunities that are apart from each other, but it does not recognize the user's perspectives in measuring attractiveness (Hanson and Schwab 1987). For example, in measuring access to bus services, a closer stop will have more attraction than a distance stop in a gravity-type measure, but it implicitly assumed that users of all categories and contexts would have the same perception of accessibility to any particular stop. This is problematic as travel impedance can widely vary in perception by the different individual for different travel contexts.
Both count- and gravity-based measurement of accessibility are reactive measures, as these measurements will tell about the status of accessibility without necessary giving a straightforward way of forecasting the changes in accessibility for changing conditions of transportation or land use system. Besides, both measures overlook the travel context (e.g. direction of a trip) entirely in quantifying accessibility and thus have the potential of over-estimating accessibility. For example, not all transit stops/stations near the individual's origin may be feasible for an individual unless they serve the individual in the direction of her/his final destination. There may be just one or two stops/stations near the origin where the individual can choose to get to the final destination. Thus, the connections between individual choices, transit network, and land-use attributes are often neglected in the count and gravity-based measures (Bhat et al. 1998).
In contrast, a utility-based measure considers the individual's perspective of ease in accessing to a service. Application of a discrete location choice model can be used to quantify the expected maximum utility of choosing a particular location. Such expected maximum utility of choice can be used as a quantitative measurement of accessibility to the service (Ben-Akiva and Lerman 1985). It is a proactive measure of accessibility as it allows for forecasting future accessibilities for any potential changes in transportation or land use system. In the context of transportation policy evaluation, it provides a method to translate accessibility to performance measures (Handy and Niemeier 1997). The concept of using the discrete choice model to quantify accessibility of not a brand-new idea, however, interestingly, very few studies implemented this method for the case of measuring accessibility to transit.
This study contributes to the literature by presenting a GIS-based tool of quantifying accessibility to transit that uses a discrete transit access stop/station location choice model for the utility-based accessibility measurement. For the empirical investigation, the paper uses the latest (2011) household travel survey data of the Greater Toronto and Hamilton Area (GTHA) to estimate a discrete choice model of walk access to transit stop/station location as a function of sociodemographic, land use and transportation-related variables. The resulting utility-based accessibility to transit measurement is integrated into a tool, which is a GIS-based traffic assignment software platform, TransCAD 7.0 (Caliper 2017) and named ‘accessibility Toolkit.’ The tool is used to empirically compare the results of three alternative approaches of access to transit measurements for the Greater Toronto Area. Besides, the estimated empirical model of access to transit stop/station choice model reveals behavioural insights into transit accessibility in Toronto.
The rest of the paper is organized as followings: the next sections present a brief literature review on measuring accessibility. This section is followed by the sections presenting discussions on the study area and data for empirical; econometric methods; empirical model of transit access station/stop location choice; development of the accessibility toolkit and empirical comparisons of alternative accessibility measurements. The paper concludes with a summary of key findings and recommendations for future research.
Section snippets
Literature review
Only a limited number of studies investigating transit accessibility using utility-based measure and capture the heterogeneity of individuals' personal and sociodemographic attributes, were found in the literature. Nassir et al. (2016) estimated an access stop choice model to compute public transit network accessibility. They adopted nested logit (NL) modelling structure, and the logsum component from the NL model is considered as an accessibility measure. In the case of choice set generation,
Study area and descriptive statistics
The GTHA consists of the city of Toronto and five regional municipalities. As per 2016 census data, its population is around 7 million which is around 20% of the total population of Canada (Statistics Canada, 2016). Nine local transit systems serve the GTHA, and Toronto Transit Commission (TTC) is the largest among them. TTC's public transit fleet is composed of bus, streetcar, and subway. The subway network is well-connected with bus and streetcar to provide better connectivity within the city
Econometric model
Multinomial logit (MNL) discrete choice model for access stop/station location choice is used for the empirical investigation (McFadden, 1973). In this modelling approach, each individual is assumed to get a certain level of utility for each access station. The total utility (Ua) of each access stop/station location is composed of systematic utility (Va) of access station and random utility components (εa) with zero mean and μ scale. The systematic utility is a function of linear-in-parameters
Empirical model of transit stop/station location choices
Two different models are estimated. The first model is the base model which excludes any socio-economic variables, and this model is estimated for all activity types. The second model includes sociodemographic attributes for all activity types. Empirical models are presented in Table 2 (Model 1) and 3 (Model 2). Variables in the final models are selected based on their statistical significance (95% confidence limit) and expected signs. Regardless, some variables with lower statistical
Comparison of alternative measures of accessibility to transit
To assess the impacts of changes to a transit system on regional accessibility, Arup (see: https://www.arup.com/en/offices/Canada/Toronto) in collaboration with Metrolinx (the Regional Planning Agency in the GTA) developed the “Accessibility Toolkit” in 2015, which is programmed as an add-on to a transportation planning software (TransCAD 7.0) considering the count-based and the gravity-based measure of accessibility (Kramer et al. 2017). In this study, the developed RUM-based measure of
Conclusions and recommendations for further research
The paper contributed to the existing literature on measuring access to transit in urban areas. This paper is mainly focused on developing a discrete choice model-based measure of accessibility to explain the first-mile issue in urban transit. Transit access stop/station location choice models were estimated using transportation level-of-service attributes different trip contexts and the sociodemographic attributes of the trip makers. The best-fitted model is integrated into an operational tool
Acknowledgements
The research was funded by an NSERC Engage Grant. Authors acknowledge the valuable suggestions and comments of Yan (Tony) Zhuang and Islam Kamel during the study. However, all comments and interpretations are of the authors only.
References (36)
- et al.
Influence of parking on train station choice under uncertainty for park-and-ride users
Procedia Manufactur.
(2015) - et al.
Modeling experienced accessibility for utility-maximizers and regret-minimizers
J. Transp. Geogr.
(2011) - et al.
Modelling the joint access mode and railway station choice
Transp. Res. E Logistics Transport. Rev.
(2009) - et al.
The cost of equity: Assessing transit accessibility and social disparity using total travel cost
Transp. Res. A Policy Pract.
(2016) - et al.
Using GIS for measuring transit stop accessibility considering actual pedestrian road network
J. Public Transp.
(2010) - et al.
Accessibility evaluation of land-use and transport strategies: review and research directions
J. Transp. Geogr.
(2004) - et al.
Do cities deserve more railway stations? The choice of a departure railway station in a multiple-station region
J. Transp. Geogr.
(2014) - et al.
What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling
Transp. Policy
(2018) - et al.
Determinants of travel mode choices of post-secondary students in a large metropolitan area: the case of the city of Toronto
J. Transp. Geogr.
(2018) - et al.
A utility-based travel impedance measure for public transit network accessibility
Transportation Research Part A: Policy and Practice
(2016)