Ahmed Osman Idris

Investigating the factors affecting transit user loyalty

Public transit agencies are constantly looking for ways to increase their ridership. While many studies have attempted to identify the factors affecting new customer attraction, the issue of transit user loyalty has been far less researched. In addition to being a good indicator of a transit agency’s performance, customer loyalty provides several added benefits. Loyal customers are more likely to use the transit agency’s services and recommend them to potential new users. Furthermore, attracting users usually involves additional customer acquisition costs (e.g. marketing) not required in order to retain existing loyal users. This study used data provided by a mixed Stated Preference/Revealed Preference survey to identify some of the factors that affect customer loyalty in the context of public transit. Factors examined include service attributes, trip characteristics, as well as socioeconomic and psychological attributes of the individual. The findings suggest that service quality attributes play a critical role in transit user loyalty, while initiatives such as the provision of real-time information panels or making park and ride facilities available have a less determinant effect on the customers’ mode shifting decisions, irrespective of their emotional response to public transit.

Dissecting the Role of Transit Service Attributes in Attracting Commuters Lessons from a Comprehensive Revealed Preference-Stated Preference Study on Commuting Mode-Switching Behavior in Toronto, Ontario, Canada

An investigation on the influence of transit service attributes on mode switching toward local transit for home-based commuting trips involved the design, implementation, and analysis of a Commuting Survey for Mode Shift. This survey exploits revealed preference mode choice information to build the stated preference mode switching experiments. The collected data set was used for estimating econometric choice models of mode switching toward transit. Separate models were estimated for car drivers and shared ride users. The empirical models showed that travel cost and in-vehicle travel time were of lower importance compared with other transit level-of-service attributes such as crowding level and number of transfers. That commuters prefer rail-based transit modes (e.g., subway and light rail transit) to other transit options (e.g., bus rapid transit) was evident. The developed models can enrich the transit service planning toolbox for delivering more efficient and attractive services that maximize transit ridership along with other objectives.

Semantic Approach to Capture Psychological Factors Affecting Mode Choice: Comparative Results from Canada and Chile

Abstract Purpose — Study the causal effect of psychological factors on mode choice, using an instrument which gathered attitudinal, affective and habitual behaviour factors, with an application to a Canadian and Chilean sample. Approach — Ad hoc questionnaire used to collect information related to psychological factors, studying the role of these factors upon mode choice using structural equation modelling, combining a measurement model and the latent variables. Findings — Emotional (affective) factors have a strong influence on mode choice, as well as attitudinal and habitual factors. Car users have a strong positive emotion to the transport mode they use when compared with public transport users and transit utilization. Research limitations — The inclusion of social factors, such as norm, role and self-concept, is desirable for a better understanding of peoples behaviour regarding transport mode usage. The structural equation modelling was used to analyse the causal effects among factors, but it is not intended to model mode choice. An advanced approach would be to estimate a hybrid discrete choice model. Practical implications — Realizing the real importance of personal psychological factors on mode choice is a key issue when intending to implement mobility and travel demand management strategies. The success of these strategies strongly relies on peoples change of behaviour, which does not depend only on instrumental and socio-demographic factors, such as cost, time and income, but also on these very inner personal aspects. Originality/value — Capture psychological factors through a comprehensive survey, which rests on a psychological framework and considers simultaneously attitudinal, affective and habitual personal factors, as well as instrumental and socio-demographic information. Make a comparison among cities belonging to different countries regarding the role of these factors, cities with a different cultural and social background.

Investigating the effects of psychological factors on commuting mode choice behaviour

This paper utilizes socio-psychometric survey data to investigate the influence of attitudes, affective appraisal and habit formation on commuting mode choice. The data-set was collected in 2009–2010 in Edmonton, Alberta. In addition to conventional socio-economic, demographic and modal attributes, the survey gathered psychological information regarding habitual behaviour, affective appraisal and personal attitudes. Different psychometric tools were used to capture psychological factors affecting mode choice. Habitual behaviour was measured using Verplankens response-frequency questionnaire. Affective appraisal was indirectly estimated using the Osgoods semantic differential. Five-point Likert scales were used to measure attitude. The structural equation modelling (SEM) approach was used to investigate the effects of psychological factors on mode choice behaviour. SEM captures the latent nature of psychological factors and uses path diagrams to identify the directionality as well as intensity of the relationships. The investigation reveals that passengers have positive emotions towards their chosen mode. Further, evidence of the superiority of the car as a travel alternative was established in terms of strong habit towards it, such that passengers would use the car for almost every single trip.

TRIBUTE: Trip-based urban transportation emissions model for municipalities

ABSTRACT This research aims at developing modeling and scenario-comparison tools to explore the impacts of various transportation and land use planning policies on changing travel behavior and eventually greenhouse gas (GHG) emissions. A Trip-Based Urban Transportation Emissions (TRIBUTE) model is developed. Data required for TRIBUTE comes from household travel surveys and emissions inventories, which is a major advantage in cases where a detailed transportation network model is unavailable. TRIBUTE is composed of two main parts: a mode choice model and an emissions forecasting model. The mode choice model is responsible for estimating modal shares of alternative modes of travel in response to changes in personal, modal, and land use attributes. The emissions forecasting model translates the modal shares into vehicle kilometers traveled, and subsequently GHG emissions. TRIBUTE is a macroscopic model intended to assist municipalities evaluate alternative transportation and land use policy scenarios and eventually select the one(s) that help them meet their future GHG emission targets. This paper reports on the conceptual framework of the developed model and presents a case study.

Demand-Sensitive Candidate Route Generation Algorithm

Demand satisfaction is a key component that reflects the quality of public transit from the passenger’s perspective, whereas cost minimization is considered a main objective of transit agencies. This trade-off between quality and cost turns transit network design into a multiobjective problem in which the interests of passengers and operators conflict. Transit network design involves the determination of various design elements, such as route alignments and stop locations, which are essential to serve transit demand within a particular area. The design of a transit network typically starts with the generation of a set of potential routes through the use of a candidate route generation algorithm. Existing route generation algorithms find the shortest path between a route’s origin and destination. Demand is aggregated without proper attention given to its pattern and distribution along the generated route. Given that demand actually is scattered along the transit route, the aggregate demand assumption is considered a major drawback of existing route generation algorithms. In an attempt to fill the highlighted gap in current practice, this paper presents a novel demand-sensitive candidate route generation algorithm that can address passenger and operator needs in a simple, objective function that aims to maximize route-level ridership. The proposed approach is well suited to small and rural communities and specialized transit services (e.g., flex route, demand responsive service) in which transit demand is dispersed.

Modeling the Impact of Transit Fare Change on Passengers’ Accessibility

Accessibility “to” and “through” public transit has been one key transit planning indicator that reflects service quality. Occasionally, transit agencies may consider a fare change to maintain operations or to attract more passengers. However, transit agencies do not usually consider the effect of such fare change on passengers’ accessibility. This paper investigates that effect. A multinomial logit mode choice model is developed to measure the monetary value of transit users’ travel time. Then, the cumulative opportunity measure of accessibility is used to examine the change in job accessibility after a recent transit fare increase in the city of Kelowna, British Columbia, Canada. The results show that the loss in job accessibility resulting from transit fare increase is inversely proportional to the length of the trip, given a flat fare structure. The findings of this paper should be kept in mind before a transit agency rethinks transit fare structures. For example, a transit agency could consider applying a zone-based fare structure as opposed to a flat fare structure to ensure better equity for all transit users.