Market-Segmentation Study of Future and Potential Users of the New Réseau Express Métropolitain Light Rail in Montreal, Canada

2021 ◽  
pp. 036119812110145
Author(s):  
Nicolette Dent ◽  
Leila Hawa ◽  
James DeWeese ◽  
Rania Wasfi ◽  
Yan Kestens ◽  
...  
Keyword(s):  
Market Segmentation ◽  
Public Transport ◽  
Public Transit ◽  
Transport Infrastructure ◽  
Light Rail ◽  
Urban Core ◽  
New Public ◽  
Key Characteristics ◽  
Transit Agencies ◽  
Under Construction

Goals for public transit agencies and new public transport infrastructure projects include attracting new riders and retaining existing system users. An understanding of the public transport market and its preferences, habits, and attitudes can help public transit agencies reach these goals by shedding light on how to increase customer satisfaction. To understand potential users of one of Montreal’s most recent major transport projects, the Réseau express métropolitain (REM), we conducted a survey in Fall 2019 while the light-rail system was under construction. Drawing on vetted transport market-segmentation frameworks, this study employs an exploratory factor analysis to reveal factors that affect respondents’ propensity to use the REM. A k-means cluster test is applied to the factors to articulate market segments. The analysis returned four clusters that form a clear spectrum of least likely to most likely REM users: car-friendly non-users, urban core potential users, transit-friendly users, and leisure and airport users. Positive opinion, proximity, and desire to use the REM for leisure or non-work trips are three key characteristics of likely users. There is a visible relationship between clusters who are likely to use the REM and clusters who agree that the REM will benefit their neighborhood. Improving people’s perception of the potential benefit of the REM to their neighborhood, better accommodating leisure use, emphasizing and communicating appealing destinations, and highlighting transit connections are four core ways that planners could work to potentially increase the number of people who are likely to use the REM.

scholarly journals Transit Signal Priority in Smart Cities

2020 ◽  
Author(s):  
Bahman Moghimi ◽  
Camille Kamga
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transit ◽  
Smart Cities ◽  
Light Rail ◽  
Service Reliability ◽  
Transit Signal Priority ◽  
Vicious Cycle ◽  
Urban Networks ◽  
Transit Agencies

Giving priority to public transport vehicles at traffic signals is one of the traffic management strategies deployed at emerging smart cities to increase the quality of service for public transit users. It is a key to breaking the vicious cycle of congestion that threatens to bring cities into gridlock. In that cycle, increasing private traffic makes public transport become slower, less reliable, and less attractive. This results in deteriorated transit speed and reliability and induces more people to leave public transit in favor of the private cars, which create more traffic congestion, generate emissions, and increase energy consumption. Prioritizing public transit would break the vicious cycle and make it a more attractive mode as traffic demand and urban networks grow. A traditional way of protecting public transit from congestion is to move it either underground or above ground, as in the form of a metro/subway or air rail or create a dedicated lane as in the form of bus lane or light rail transit (LRT). However, due to the enormous capital expense involved or the lack of right-of-way, these solutions are often limited to few travel corridors or where money is not an issue. An alternative to prioritizing space to transit is to prioritize transit through time in the form of Transit Signal Priority (TSP). Noteworthy, transit and specifically bus schedules are known to be unstable and can be thrown off their schedule with even small changes in traffic or dwell time. At the same time, transit service reliability is an important factor for passengers and transit agencies. Less variability in transit travel time will need less slack or layover time. Thus, transit schedulers are interested in reducing transit travel time and its variability. One way to reach this goal is through an active intervention like TSP. In this chapter a comprehensive review of transit signal priority models is presented. The studies are classified into different categories which are: signal priority and different control systems, passive versus active priority, predictive transit signal priority, priority with connected vehicles, multi-modal signal priority models, and other practical considerations.

Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform

2020 ◽  
Vol 54 (24) ◽  
pp. 15613-15621
Author(s):  
Derek V. Mallia ◽  
Logan E. Mitchell ◽  
Lewis Kunik ◽  
Ben Fasoli ◽  
Ryan Bares ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Public Transit ◽  
Light Rail

scholarly journals Understanding Users’ Satisfaction Towards Public Transit System in India: A Case-Study of Mumbai

2021 ◽  
Vol 10 (3) ◽  
pp. 155
Author(s):  
Rahul Das
Keyword(s):  
Service Quality ◽  
Public Transport ◽  
Travel Demand ◽  
Resource Constraints ◽  
Public Transit ◽  
Sampling Bias ◽  
Relevant Information ◽  
Transit System ◽  
Transport Services ◽  
Developing Cities

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score.

scholarly journals Evaluating Performance of Public Transport Networks by Using Public Transport Criteria Matrix Analytic Hierarchy Process Models—Case Study of Stonnington, Bayswater, and Cockburn Public Transport Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6949
Author(s):  
Gang Lin ◽  
Shaoli Wang ◽  
Conghua Lin ◽  
Linshan Bu ◽  
Honglei Xu
Keyword(s):  
Sustainable Development ◽  
Public Transport ◽  
Public Transportation ◽  
Evaluation Criteria ◽  
Performance Standards ◽  
Transport Network ◽  
Process Models ◽  
Transport Infrastructure ◽  
Transport Networks ◽  
Ahp Model

To mitigate car traffic problems, the United Nations Human Settlements Programme (UN-Habitat) issued a document that provides guidelines for sustainable development and the promotion of public transport. The efficiency of the policies and strategies needs to be evaluated to improve the performance of public transportation networks. To assess the performance of a public transport network, it is first necessary to select evaluation criteria. Based on existing indicators, this research proposes a public transport criteria matrix that includes the basic public transport infrastructure level, public transport service level, economic benefit level, and sustainable development level. A public transport criteria matrix AHP model is established to assess the performance of public transport networks. The established model selects appropriate evaluation criteria based on existing performance standards. It is applied to study the Stonnington, Bayswater, and Cockburn public transport network, representing a series of land use and transport policy backgrounds. The local public transport authorities can apply the established transport criteria matrix AHP model to monitor the performance of a public transport network and provide guidance for its improvement.

Transit Planning Optimization Under Ride-Hailing Competition and Traffic Congestion

2021 ◽  
Author(s):  
Keji Wei ◽  
Vikrant Vaze ◽  
Alexandre Jacquillat
Keyword(s):  
New York ◽  
Traffic Congestion ◽  
Service Providers ◽  
Public Transit ◽  
Mixed Integer ◽  
Nonlinear Program ◽  
Transit Planning ◽  
Planning Optimization ◽  
Transit Ridership ◽  
Transit Agencies

With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, we develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers’ mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, our optimized transit schedules consistently lead to 0.4%–3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers’ preferences—by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win–win–win outcome.

On-Demand Public Transit: A Markovian Continuous Approximation Model

2021 ◽  
Author(s):  
Daniel F. Silva ◽  
Alexander Vinel ◽  
Bekircan Kirkici
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Public Transit ◽  
Continuous Approximation ◽  
Efficient Operation ◽  
Fleet Sizing ◽  
On Demand ◽  
Single Vehicle ◽  
Transit Agencies ◽  
Using Data

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

scholarly journals Distributional effects of transport policies on inequalities in access to opportunities in Rio de Janeiro

2017 ◽  
Author(s):  
Rafael Henrique Moreas Pereira ◽  
David Banister ◽  
Tim Schwanen ◽  
Nate Wessel
Keyword(s):  
Public Transport ◽  
Rio De Janeiro ◽  
Spatial Regression ◽  
Developed Countries ◽  
Transport Infrastructure ◽  
Future Research ◽  
Distributional Effects ◽  
Spatial Inequalities ◽  
Bus Services ◽  
The City

The evaluation of the social impacts of transport policies is attracting growing attention in recent years. Yet, this literature is still predominately focused on developed countries. The goal of this research is to investigate how investments in public transport networks can reshape social and geographical inequalities in access to opportunities in a developing country, using the city of Rio de Janeiro (Brazil) as a case study. Recent mega-events, including the 2014 Football World Cup and the 2016 Olympic Games, have triggered substantial investment in the city’s transport system. More recently, though, bus services in Rio have been rationalized and reduced as a response to a fiscal crisis and a drop in passenger demand, giving a unique opportunity to look at the distributional effects this cycle of investment and disinvestment have had on peoples’ access to educational and employment opportunities. Based on a before-and-after comparison of Rio’s public transport network, this study uses a spatial regression model and cluster analysis to estimate how accessibility gains vary across different income groups and areas of the city between April 2014 and March 2017. The results show that recent cuts in service levels have offset the potential benefits of newly added public transport infrastructure in Rio. Average access by public transport to jobs and public high-schools decreased approximately 4% and 6% in the period, respectively. Nonetheless, wealthier areas had on average small but statistically significant higher gains in access to schools and job opportunities than poorer areas. These findings suggest that, contrary to the official discourses of transport legacy, recent transport policies in Rio have exacerbated rather than reduced socio-spatial inequalities in access to opportunities. These results also suggest that future research should consider how the modifiable areal unit problem (MAUP) can influence the equity assessment of transport projects.

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