Modelling Train Station Choice for Park and Ride Users Based on the Effect of Crowding on Trains

Modelling train station choice for park and ride users based on the effect of crowding on trains

Abstract

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Crowding on trains has become one of the biggest public transport problems in Australian major cities. However, limited knowledge in the literature is available on how the overcrowding on trains influences park and ride (PnR) user’s choice of train stations. This paper presents an extended expected utility model and latent class model to explore PnR users’ station choice behaviour under the effect of crowding and measured their risk attitude towards crowding measures. The station choice under the risk of crowding behaviour data were collected using revealed preference (RP) and stated preference (SP) surveys. The questionnaires of the SP survey were designed based on D-efficiency.   The results revealed how crowding attributes, such as in vehicle travel time, the percentage that seats have been taken  the density of standee and the number of days per week on which the trains are too crowded to board, affect their train station choice.   We also found that the more crowded on trains, the more risk aversion attitudes towards crowding that respondents have. Furthermore, the data implied that difference of individual’s annual income would influence the heterogeneity of individual’s station choice under the risk of crowding. Based on these, we may suggest a graded train ticket fare system is put in place to make rail travel more attractive to higher income passengers.

Key words: crowding, station choice, park and ride users, risk attitude, extended expected utility theory, latent class model

1. Introduction

Park and Ride (PnR) has been widely accepted as a means to encourage car drivers to combine public transport and private cars. It can reduce traffic demand (e.g. travel and parking demand) in the central of city by providing car parking facilities well outside the central area of the city and linking the facilities to the central city by public transport services. Correspondingly, it can decrease traffic congestion, energy use, emission levels, and other environmental impacts and keep urban sustainable development (Ginn 2009). According to the data from the Transport (2010), the capacity of PnR facilities in Perth, WA is only 17,000. It is inadequate to meet the overall PnR demand in Perth, Western Australia. However, a survey, conducted in 2 July, 2012 by jointing of the University of Western Australia, Curtin University, the Department of Planning (DoP), the Department of Transport (DoT) and Public Transport Authority (PTA), revealed that the demand for PnR facilities distributed unevenly. So far, it is not clear why some stations with PnR facilities are more likely to be chosen by commuters than the others.

Many factors, such as, parking availability, parking search time, travel time to access train stations and crowding on trains, have been identified to influence commuters’ choice (T.Lin and N.H.M.Wilson 1992; Anthony Chen, Ji and Recker 2001; Peter van der Waerden, Aloys Borgers  and Timmermans 1998; Hess 2001). Crowding on trains is one of the most frequently encountered by Australian and other countries’ passengers (Thompson et al. 2012; Cox, Houndmont and Griffiths 2006). Generally, its effect can be concluded into two categories: 1) the effects on passengers and 2) the effects on operators. From a passenger’s perspective, these effects can be further divided into physical or psychological effects. T. Cox, J. Houdmont, and Griffiths (2006) and D. Katz (2010) stated that crowdedness on trains were perceived by railway commuters as serious safety and security issues;, Lundberg (1976), N. D. Mohd Mahudin, T. Cox1, and Griffiths (2011) found high density of passengers could increase anxiety and stress levels measured by the rate of catecholamine excretion. The discomfort feelings grew more intense as the density of passengers increased. T. Cox, J. Houdmont, and Griffiths (2006) and Gregory J. Nicosia et al. (1979) found that the stress related to rail travel has more variations in regards to the social and ecological trip conditions than the length or duration of trips. Kanhneman et al. (2004) explored its effect on commuters’ health. N. D. Mohd Mahudin, T. Cox1, and Griffiths (2011) showed evidence that crowding could lead to more somatic symptoms such as headaches, tension, stiff muscles, and sleeplessness.

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Alejandro Tirachini, David A. Hensher, and Rose (2013) found that crowding could increase riding time, boarding time, alight time and waiting time due to friction among passengers as well and asserted crowding inside buses might be more problematic for alighting than for boarding. Fernández (2011) also achieved the similar result with a laboratory experiment. Overcrowding on a train can not only increase in-vehicle travel time and waiting time, but also influence travel time reliability which would frustrate commuters with uncertain arrival time for work. Correspondingly, crowdedness can affect commuters’ travel choice. Currently, only rout choice, travel mode choice and departure time choice were found. A. Sumalee, Z. Tan, and Lam (2009), Leurent and Liu (2009),Y. Hamdouch et al. (2011), and J.-D. Schmöcker et al. (2011) developed disutility functions for understanding the impact of crowding on passengers’ mode choice and access station choice. S. Raveau, J.C. Muñoz, and Grange (2011) also established a route choice model for public transit networks to explore the effect of overcrowding on train carriages on choosing a route in a transit network by public transit users; Joon-Ki, Backjin Lee, and Oh (2009) established a bus choice model with a binary logit design explaining why commuters did not choose the first bus with high occupancy rate. Bill Davidson et al. (2011) established a new strategic transport model, including capacity and crowding modules, for Metro network transport model (MNTM) to predict ridership.

From the operators’ perceptive, overcrowding could affect operating speed and cost and public transport ridership. Therefore, crowding was often considered as an index for evaluating public transport service quality. Some intervention strategies, such as increasing service frequency and enlarging vehicle size, can be implemented for further improvement. Batarce, Muñoz, and Ortúzar (2016) explored the effect of crowding on the public transport system’s demand and users’ benefit by comparing outcomes from three transport policies improving bus corrido operations. They found increasing frequency would overestimate demand and users’ benefit. Alejandro Tirachini, David A. Hensher, and Rose (2013) summarised the effect of crowding on public transport system’s reliability, optimal supply and pricing. Based on these, they suggested public transport operators should determine the service frequency and capacity with consideration of effect of crowding.

Crowding on trains was measured objectively and subjectively. Objective measurement is diverse. The most common metrics used in quantitative assessment include load factor, which is the ratio of the actual number of passengers inside vehicles to the number of seats (Whelan and Crockett, 2009); passenger loading based on levels of service (Lam, Cheung and Lam 1999); the percentage of standee class passengers standing (Sarah Blunden et al. 2011); standing passenger area (i.e., space (m²) per standing passenger); the number of standing passengers per square meter; the rolling hour average loads and the length of standing time (zheng Li and Hensher 2013). Given that these objective measurements cannot capture individual’s perception for crowding, some researchers suggested the crowding should be two dimensions including objective and subjective measurements and subjective measurements can be influenced by physical antecedents, interpersonal, individual characteristics and modifiers (Turner et al. 2004; Sundstrom, Busby and Asmus 1975). However, few quantitative metric of subjective crowding was identified to date (zheng Li and Hensher 2013).

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The previous literature on station choice is also limited, especially for the study of the influence of crowding. Currently, only one nested logit model for access station choice, developed by Debrezion, Pels, and Rietveld (2009), was found. It only can test the effect of the accessibility indicator on station choice, rather the effect of crowding on trains directly, given that crowding measures were integrated with other factors into the accessibility indicator, rather an independent attribute contributing to the alternative’ utility. Other station choice models were developed within the discrete choice theory as well even though crowding was not taken as a factor. The simplest model is the linear logit model developed by Kastrenakes (1988) based on location of station, access time, frequency of service and generalised cost. This model is mainly used for prediction of rail ridership to specific stations by NJ transit. More complicated models, such as nested and cross nested logit models, were developed by some researchers (Fan, Miller and Badoe 1993; Davidson and Yang 1999; Lythgoe and Wardman 2004; Lythgoe, Wardman and Toner 2004; Wardman 1997) were applied to station choice modelling. In general, all current models were developed within discrete choice theory and aimed to understand the relationship between access mode and train station choice to forecast train ridership. In other words, the effect of crowding on station choice hasn’t been carefully studied so far.

Therefore, this paper focused on modelling train station choice under the effect of crowding for PnR users within discrete choice theory. Based on this model, we can estimate the effect of crowding on station choice directly and measured the PnR users’ risk attitude towards crowding in typical week day.

The rest of the paper is structured as follows. The section 2 discusses data collection methods, followed by the modelling methodology in section 3. The estimation results and the implications of the analysis for transportation planning are presented in section 4. The section 5 elaborates the model validation. Finally, in Section 6, the paper is concluded with some remarks, conclusions and limitations.

important findings and limitation.

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2. Data collection

2.1 Attribute identification

To quantitatively depict the effect of crowding on station choice for PnR users, four attributes were identified as the most important factors in the paper based on previous literature. They are the percentage that seats have been taken, the density of standee in a train carriage during normal situations, the number of days per week on which the trains are too crowded to board and in vehicle travel time. The first two factors belong to load factors, the third one indicates the probability of not being able to board on trains due to overcrowdings. The extreme situation in the paper is identified based on the crush capacity proposed by Conner (2011) and crowding rates suggested by Nicholls (2017).   The crush capacity proposed by Conner (2011) is 7 passengers/m² and the crowding rates is 1.25, so the maximum density of standee in a train carriage is 8 persons/m². After all seats were taken, the crush density of standee in a carriage is 8 persons/m².  This means it is too crowded for anyone to board trains. Attributes and their levels are listed in the table 1.

Table 1. Attributes and attribute levels