Public Transport Planning Research Articles

Stakeholder experiences of using online spatial data visualisation tools for local public health decision support: A qualitative study.

The distribution of food outlets within towns and cities and the provision of active travel infrastructure have been associated with health behaviours that can contribute to obesity risk. Decision-makers describe a lack of local data and research evidence as a barrier to policy adoption to improve the public's health. Online spatial data visualisation tools created by researchers can help to bridge this gap. We explored stakeholder experiences of using such tools for decision-support, with a focus on facilitators and barriers to use. We conducted 16 qualitative interviews with Public Health, Planning and Transport Planning professionals, who had used two recently-developed tools. Participants described the importance of tools being open access; their use in “story-telling”, particularly to non-experts; and more broadly their use even when imperfect. They expressed that ‘robustness’ of underpinning data was important, however this was not easily defined. Participants employed personal heuristics, principally based on endorsement and developer credibility, to determine suitability for use. We present key learning points for future developers to maximise engagement and impact.

Transit Networks, Social Contacts, and Open Data Meet Public Transportation Plans for Post-COVID-19: A Canadian Case Study

The emergence of COVID-19 and its variants has dramatically shifted the way that societies respond to a pandemic crisis and postpandemic plans. One of the response needs is the ability to track potential transmissions within population movements effectively, which can exploit the means of pervasive computing by collecting and processing ubiquitously acquired anonymously aggregated data, as well as long-term data that constitute prior but limited contextual knowledge. This article presents a practical method for the assessment of the risk profiles of communities by acquiring, fusing, and analyzing data from public transportation, district population distribution, passenger interactions, and cross-locality travel data while still preserving individual user privacy. The proposed framework fuses these data sources into a realistic simulation of a transit network for a given time span to report on the risk of the public transit system as whole, as well as problematic wards and routes. By shedding credible insights into the impact of public transit on pandemic spread, the article findings will help to set the groundwork for aggregate transmission simulation tools that could provide pandemic response teams with a robust framework for the evaluations of city districts most at risk, and how to adjust municipal services accordingly.

Vehicle trajectory-clustering method based on road-network-sensitive features

Existing trajectory-clustering methods do not consider road-network connectivity, road directionality, and real path length while measuring the similarity between different road-network trajectories. This paper proposes a trajectory-clustering method based on road-network-sensitive features, which can solve the problem of similarity metrics among trajectories in the road network, and effectively combine their local and overall similarity features. First, the method performs the primary clustering of trajectories based on the overall vehicle motion trends. Then, the map-matched trajectories are clustered based on the road segment density, connectivity, and corner characteristics. Finally, clustering is then merged based on the multi-area similarity measure. The visualization and experimental results on real road-network trajectories show that the proposed method is more effective and comprehensive than existing methods, and more suitable for urban road planning, public transportation planning, and congested road detection.

Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service

The regularity of urban mobility is a tacit understanding in public transportation planning. Many studies investigated regularity in the context of individual travel while fewer were found on aggregate mobility. This work proposes a generic measure of regularity to quantify the degree of repetition of a time or spatial data series. In addition, it discusses how the properties of a data series itself may dictate the regularity and adopts parametric bootstrapping to estimate the residual regularity that deviates from realizations of stochastic processes. The proposed measure can examine not only the periodicity of trip generation in locations but also the stationarity of travel flows. The experiments were conducted on a ridesourcing dataset, including more than six million trips in Chengdu, China. Both the temporal and spatial regularities were investigated at distinct time intervals. We discussed how the total travel volume and the peakedness of probability distribution could affect the regularity. It was found that regularity was positively associated with the total volume and the peakedness. We further examined the impact of the built environment on regularity and the effects of spatial and temporal scales. The results show both point-of-interest density and diversity and tourist attractions contribute to regularity. Regularity grows linearly as the spatial and temporal scales increase exponentially. Lastly, different specifications of regularities were compared, and the outcome was generally consistent across different similarity measures including coefficient of determination, Manhattan distance, Euclidean distance, cross-entropy and cosine similarity.

A Study on Social Benefits Evaluation Method for Regional Public Transportation Planning Considering Generational Differences

A Study on Social Benefits Evaluation Method for Regional Public Transportation Planning Considering Generational Differences

The Clustered Dial-a-Ride Problem

We study a variant of the classical dial-a-ride problem, with an application to public transport planning in rural areas. In the classical dial-a-ride problem, n users each specify a pickup and a delivery location, and the aim is to plan the least cost route to cater all requests. This can be modeled as a traveling salesmen problem in a complete graph with precedence constraints (pick-ups need to happen before deliveries). In this paper, we consider the clustered dial-a-ride problem, where we do not operate on a complete graph but on a graph composed of serially numbered cliques where each clique is connected to the next one via a single edge. This setting is inspired by door-to-door transportation for people from remote villages who want to get to another village or the next city by a bus which operates on demand. We argue that in case the optimal route exhibits certain structural properties, it can be computed significantly faster. To make use of this observation, we devise a classification algorithm which can decide whether the optimal route exhibits these structural properties before computing it. Extensive experiments on artificial and real-world instances reveal that the majority of optimal routes indeed have the desired properties and that our classifier is an efficient tool to recognize the respective instances.

Exploring Equity in Public Transportation Planning Using Smart Card Data.

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

Identifying critical metro stations in multiplex network based on D–S evidence theory

Public transport networks (PTNs) undertake large amount of passenger demand in the urban transport system. Particularly, stations in metro networks with high significance in structure and function are more likely to contribute to passenger transport. Once these stations are under functional failure, it is easy to lead to the collapse of network connectivity. Thus, identifying critical nodes in PTNs is of practical significance for the public transport planning and operation. This study proposes an identification method for the critical nodes in multiplex network by considering the interaction between metro and bus networks. First, metro and bus networks are constructed by L-space and P-space methods, respectively. Then, ridership is extracted to describe the connection between nodes as the weights of links, and the metro-bus multiplex network is then constructed. Furthermore, the identification method in Multiplex Network based on Dempster–Shafer evidence theory (MNDS) is proposed to fuse the significance of nodes in sub-networks, and the critical nodes in the multiplex network are identified. Finally, the PTNs in Shenzhen City, China, is used as a case to demonstrate the feasibility of MNDS. By attacking critical nodes, a comparison is conducted and compared with two traditional identification methods (Weighted Closeness Centrality and Technique for Order Preference by Similarity to Ideal Solution) using two indicators, global efficiency (GE) and the size of the largest connected component (LCC). The results indicate the critical nodes identified by MNDS are of greater significance than those identified by the other two methods. This study provides a feasible method for critical nodes identification in urban public transport system, which can be applied to public transport operation and planning.

Sustainable Public Transportation Evaluation using a Novel Hybrid Method Based on Fuzzy BWM and MABAC

Background: The transportation sector has wide-ranging effects on the human societies. Public transportation has a key and undeniable role in the lives of people in society and affects important aspects such as economic, social, cultural and environmental. Therefore, assessing the sustainability of public transportation in urban areas can be considered as a challenge for transportation policy makers. Methods: In this study, a novel hybrid multi-criteria decision-making (MCDM) method is proposed to evaluate sustainable public transportation in Tehran. Evaluation criteria have been identified using the literature and experts’ opinion. The proposed method integrates the fuzzy best-worst method (FBWM) and the multi-attributive border approximation area comparison (MABAC) method. a group of three experts determined the weight and importance of each criterion using FBWM. The MABAC method was then used to rank sustainable public transport alternatives. Results: The results indicate the reliability of the proposed method. Also, we can see that the results are congruent with the actual conditions of public transportation. The studied alternatives have been evaluated, and according to the decision criteria, metro and e-hailing have been the most sustainable alternatives. It is noteworthy that the economic and financial sustainability, service availability and environment sustainability have been the most important criteria. Conclusion: The proposed framework in this study can be used by public transportation planners and policy makers to identify sustainable options in order to consider facilities and implement incentive policies in this field. Also, the results of the proposed method used in this study can be used as a suitable guidance to assess the sustainability of public transportation.

Medium-term public transit route ridership forecasting: What, how and why? A case study in Lyon

Demand forecasting is an essential task in many industries and the transportation sector is no exception. This is because accurate forecasts are a fundamental aspect of any rationale planning process and an essential component of intelligent transportation systems. In the context of public transit, forecasts are needed to support different level of planning and organisational processes. Short-term forecast, typically a few hours in the future, are developed to support real-time operations. Long-term forecast, typically 5 years or more in the future, are essential for strategic planning. Those two forecast horizons have been widely studied by the academic community but surprisingly little research deal with forecast between those two ranges. The objective of this paper is therefore twofold. First, we proposed a generic modelling approach to forecast next 365 days ridership in a public transit network at different levels of spatiotemporal aggregation. Second, we illustrate how such models can assist public transit operators and transit agencies in monitoring ridership and supporting recurrent tactical planning tasks. The proposed formulation is based on a multiplicative decomposition that combines tree-based models with trend forecasting. The evaluation of models on unseen data proves that this approach generates coherent forecast. Different use cases are then depicted. They demonstrate that the resulting forecast can support various recurrent tactical tasks such as setting future goals, monitoring ridership or supporting the definition of service provision. Overall, this study contributes to the growing literature on the use of automated data collection. It confirms that more sophisticated statistical methods can help to improve public transportation planning and enhance data-driven decision making.

The effects of COVID-19 epidemic on public transport ridership and frequencies. A case study from Tampere, Finland

The COVID-19 epidemic has created unforeseen effects in public transport in cities around the world, including Tampere, where a 70% decrease in the number of passengers occurred in spring 2020. The purpose of this paper is to study changes in public transport ridership, frequencies, and average fill rates during the epidemic in May 2020 compared to normal circumstances in January 2020 using map-based analysis. We used data provided by Tampere regional transport to create the public transport network and to assess the frequencies, ridership and average fill rates in different areas of Tampere. The paper presents the method used to analyze how the modified frequencies meet the decreased demand of public transport in Tampere. These results indicate that the decrease in ridership was great in almost all areas, except some eastern parts of Tampere. The frequencies were decreased in all areas but kept at a sufficient level. When analyzing fill rates, we found that the bus lines coming from east of Tampere were more crowded on average during the COVID-19 epidemic in May compared to January. In other areas, fill rates were lower. The results suggest that in Tampere, frequencies were mostly managed to maintain at a sufficient service level. However, the analyses also reveal that frequencies were not adjusted successfully in all areas with high fill rates in some routes. It is important to notice, that the public transport planners were facing a deviant situation with COVID-19 and that in future, there will be more information to help decision-making.

Timetabling for strategic passenger railway planning

In research and practice, public transportation planning is executed in a series of steps, which are often divided into the strategic, the tactical, and the operational planning phase. Timetables are normally designed in the tactical phase, taking into account a given line plan, safety restrictions arising from infrastructural constraints, as well as regularity requirements and bounds on transfer times.In this paper, however, we propose a timetabling approach that is aimed at decision making in the strategic phase of public transportation planning and to determine an outline of a timetable that is good from the passengers’ perspective. Instead of including explicit synchronization constraints between train runs (as most timetabling models do), we include the adaption time (waiting time at the origin station) in the objective function to ensure regular connections between passengers’ origins and destinations. We model the problem as a mixed integer quadratic program and linearize it. Furthermore we propose a heuristic to generate starting solutions. We illustrate the trade-offs between dwell times and regularity of trains in two case studies based on the Dutch railway network.

Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

A Procedure to Estimate Air Conditioning Consumption of Urban Buses Related to Climate and Main Operational Characteristics

Public Transport (PT) planning requires a detailed evaluation of the fleet energy consumption, usually depending on the specific characteristics of the bus service network. These include topography, climate and operational features. In this work, we focus on the additional air conditioning (AC) energy consumption, proposing a method to evaluate the extra energy consumption based not only on climate variables, but also on the PT planned service. Results are presented for a large part of the provincial capitals and regions of Italy, and clearly show that overconsumption for air conditioning are significantly affected by the daily mileage distribution, with large variance even when climatic conditions are similar. The mileage data are extracted from GTFS databases, widely available for PT applications. The developed tool allows us to apply this methodology to any urban and extra-urban area. Reference AC consumption related to climate conditions are derived from a measurement campaign hold in Cagliari (Sardinia, Italy) during September 2018, within the National Research Program on the Electric System. A discussion on how to optimize the use of climatic data is also presented, resulting in the choice to use Heat Index as unique independent variable for air conditioning energy consumption calculation. A methodology to compute the Heat Index from climatic variables for large domains as, for instance, the Italian regions, was also developed.

Spatial and socio-economic analysis of public transport systems in large cities: A case study for Belo Horizonte, Brazil

The main goal of this article is to evaluate the relationship between the spatial distribution of the population of the city of Belo Horizonte, one of the largest cities in South America, and its public transport network, seeking to ascertain whether the current bus network is able to meet the needs of the population. The methodology was based on data processing and vector information from different sources through spatial analysis tools. As expected, the results revealed a greater concentration of routes and bus stops in the central region of Belo Horizonte, which is the main commercial center of the municipality and its metropolitan region. However, the results suggest evidences of a disadvantage for the population with lower income, especially those living on the periphery. A possible explanation for this disadvantage is the invisibility of these communities due to their high level of irregular occupation. Under the eyes of public transport planning, they are not recognized, and, theoretically, do not generate demand for lines and bus stops

Mode choice analysis among motorcycle and Trans Koetaradja urban bus and its contributing factors using revealed preference (RP) data

The random utility maximization (RUM) is a widely used principle to study customer behaviors derived from econometric theory. The mode choice problem in transportation planning has been intensively studied to explore the choice behavior of travelers. In the demand analysis, market shares among transport modes are substantially used for public transport planning and operation. Therefore, this study is a preliminary analysis of the modeling complex mode choice behaviors among motorcycle and bus system so-called Trans Koetaradja by considering specific attributes of mode, households, and individual sociodemographic attributes. The developed model of mode choice is used to explore the effect of specific attributes of mode, households, and individual sociodemographic attributes on traveler’s mode choice behaviors and its contributing factors. Using the Revealed Preference (RP) data collected in Banda Aceh, we formulate Binary Logit (BL) to identify and evaluate the effects specific attributes of mode, households, and individual sociodemographic on mode choice. These results confirm that there is a substantial contribution of the specific mode attributes of the individual in determining selected mode choice. The specific attribute of motorcycle mode preference such as travel time (TT) and distance of destination (DD). Findings further confirm that individual sociodemographic variable including dummy young Age (DYA), dummy has elementary education background (DEB), dummy has low-medium income (DIN), dummy owned motorcycle (DOM) and dummy has driver license (DDL) have a statistically significant alert to the selecting mode choice of motorcycle.

Justice in Regional Transport Planning through the Lens of Iris Marion Young

ABSTRACT The paper investigates whether and how regional public transport plans in Sweden address issues of social justice. Drawing upon a theoretical and analytical framework based on Young’s concept of domination and using a critical discourse analysis conducted on regional transport plans in three Swedish regions (Skåne, Stockholm and Västra Götaland), we identify discourses potentially resulting from and reproducing domination dynamics. The analysis highlights the centrality of economic growth goals and the framing of public transport users as customers not involved in the decision-making processes, which contradicts the declared justice and sustainability aims of the plans.

Increasing public transport patronage – An analysis of planning principles and public transport governance in Swedish regions with the highest growth in ridership

Increasing the attractiveness of public transport is a key issue in the endeavours towards more sustainable transport systems. While there is a lot of knowledge on what can be done to increase public transport patronage, there is a lack of empirically based research analysing how to do this in practice. Using a comparative case analysis of six regions in Sweden with the highest increase in passenger volumes for public transport between 2009 and 2015, this paper examines the prerequisites for increasing public transport patronage, with a focus on the governance conditions required to implement such measures. The empirical material consists of semi-structured interviews with public transport planners and strategy documents for the six regions. The findings show that all regions but one employed a similar approach and implemented measures aimed at concentrating resources to corridors where the potential demand was the greatest. Only one region chose a different approach by investing in services in both strong routes and in the peripheral network. However, regardless of approach, the results highlight that there is considerable coherence regarding the governance conditions that enable implementation. Three main conditions were identified, namely political support, well-functioning collaboration between organisations, and public support through citizen dialogue. The results support key findings on collaborative conditions from previous research, including the importance of joint objectives, trust between key individuals, and the need for long time frames in order to develop collaborative capacity.

Preface: Public transport planning in the face of uncertainty

Preface: Public transport planning in the face of uncertainty

Spatial analysis of public transportation infrastructure in Santiago, Chile, using the continuous approximation method

Santiago, the capital city of Chile, has seven million inhabitants in an area of 850 km2. This city has a metro network with seven lines extending 140 kilometers and transports approximately 2.6 million people daily. The bus system has undergone significant transformations over the last three decades. The most relevant change having been Transantiago, the public transportation system implemented in 2007 for Santiago, Child, which combines the use of Metro and buses (BRT). Metropolitan Mobility Network (called Red) is the latest version of the public transportation plan. This paper aims to analyze the current subway infrastructure using the continuous approximation method for Santiago, Chile. We previously proposed a macroscopic methodology to identify the needs for an adequate level of service in urban mobility and transportation, and we applied it to Santiago’s Metro network. Our work focuses on functionality and demand distribution. Santiago’s demand varies spatially in volume and extension throughout the city. Using the latest origin-destination survey from 2012, we deduct the critical components in this current network structure. It is worth mentioning that the Metro design bases its network on a ring-radial structure. With our macroscopic model applied to Santiago, Chile, we have detected infrastructure needs in the current transit network. The supply of infrastructure should increase for two reasons: first, to achieve balanced cost levels between users and the agency and second, to reduce subway occupations. The optimal model outcomes for Santiago define the optimal network in which the system requires five rings and ten end-to-end longitudinal lines (20 radial routes), including lower levels of occupation. The obtained results are a good preliminary solution, considering the subway infrastructure supply could be sub-estimated in the public transportation plan.