Public Transport Demand Research Articles

On the interaction between public transport demand, service quality and fare for social welfare optimisation

This paper analyses the interaction between fares and public transport service quality. With higher fares the operator has more resources to provide a better service, while demand in turn depends on both service quality and fare leading therefore to the question as to whether there is an optimal fare for different city scales. The model builds on the work of Daganzo (2010) who determines optimal network headway, stop spacing as well as the ratio of a central dense service area compared to the whole city size. In contrast to Daganzo we include fare and demand elasticity. With this it is possible to obtain some general insights for a range of cities on what type of fare levels are favorable. We focus on a flat fare structure, finding that from the viewpoint of maximizing social welfare, lower fares are preferable. If the operator cost coverage ratio is considered as objective function then there exists an optimal fare above the minimum fare. We illustrate the possible existence of critical fare points, at which the demand and service quality of public transport suddenly drop if an attractive alternative mode exists. We discuss further the impact of city size and demand density on optimal fares and service quality.

EXPLORING THE POTENTIAL OF CROWD SOURCED DATA TO MAP COMMUTER POINTS OF INTEREST: A CASE STUDY OF JOHANNESBURG

Abstract. Modern African cities are faced with various mobility and transportation challenges. In developing smart sustainable cities, city planners need to create a balance between supply and demand for public transportation. Development of multi-mobility mode models has contemporarily received a special interest in smart cities development. Globally, the use of bike sharing services to complete the first kilometre or last kilometre of the trip has been highly received, with commuters using either rail or road mobility modes for the middle section of their trip. Within the developing world context, the use of multi-mobility modes in daily commuting is still new, and little research has been done to guide this. Notwithstanding the influence of uncertainties and fragmentation over demand and supply in public transportation provision. In the South Africa context, various modes of public transportation have been developed which seek to be smart, sustainable and efficient such as the fast train (Gautrain), Bus rapid transport (Rea Vaya and Gaubus) and Bikes sharing platforms (Upcycles), however most of these modes are currently not spatially connected. Hence the researcher sought to develop a stepping stone in planning for future mobility demand. Using an explorative methodology, the authors collected quantitative and spatial data in the form of land-use data and crowd sourced data (from twitter) to map commuter points of interest in and around the city of Johannesburg. The results reveal hot and cold spots in the city. The hot spots reveal areas where commuters frequently travel to, and when overlaid with transportation data, we are able to identify potential locations to develop new transportation hubs as these will overtime become key points of interest.

Demand management of congested public transport systems: a conceptual framework and application using smart card data

Transportation demand management, long used to reduce car traffic, is receiving attention among public transport operators as a means to reduce congestion in crowded public transportation systems. Though far less studied, a more structured approach to public transport demand management (PTDM) can help agencies make informed decisions on the combination of PTDM and infrastructure investments that best manage crowding. Automated fare collection data, readily available in many public transport agencies, provide a unique platform to advance systematic approaches for the design and evaluation of PTDM strategies. The paper discusses the main steps for developing PTDM programs: (a) problem identification and formulation of program goals; (b) program design; (c) evaluation; and (d) monitoring. The problem identification phase examines bottlenecks in the system based on a spatiotemporal passenger flow analysis. The design phase identifies the main design parameters based on a categorization of potential interventions along spatial, temporal, modal, and targeted user group parameters. Evaluation takes place at the system, group, and individual levels, taking advantage of the detailed information obtained from smart card transaction data. The monitoring phase addresses the long-term sustainability of the intervention and informs potential changes to improve its effectiveness. A case study of a pre-peak fare discount policy in Hong Kong’s MTR network is used to illustrate the application of the various steps with focus on evaluation and analysis of the impacts from a behavioral point of view. Smart card data from before and after the implementation of the scheme from a panel of users was used to study policy-induced behavior shifts. A cluster analysis inferred customer groups relevant to the analysis based on their usage patterns. Users who shifted their behavior were identified based on a change point analysis and a logit model was estimated to identify the main factors that contribute to this change: the amount of time a user needed to shift his/her departure time, departure time variability, fare savings, and price sensitivity. User heterogeneity suggests that future incentives may be improved if they target specific groups.

Bus Passenger Demand Modelling Using Time-Series Techniques- Big Data Analytics

Background: Public transport demand forecasting is the fundamental process of transport planning activity. It plays a pivotal role in the decision making, policy formulations and urban transport planning procedures. In this paper, public bus passenger demand forecasting model is developed using a novel approach. The empirical passenger demand for a bus depot is modelled and forecasted using a data-driven method. The big data generated by Electronic Ticketing Machines (ETM) used for issuing tickets and collecting fares is sourced as the data for demand modelling. This big data is time indexed and hence has the potential for use in time-series applications which were not previously explored. Objectives: This paper studies the application of time-series method for forecasting public bus passenger demand using ETM based time-series data. The time-series approach used is the four Holt-Winters’ modeling methods. Holt-Winters’ additive and multiplicative models with and without damping have been empirically compared in this study using the data from the inter-zonal buses. The data used in the study is a part of the transaction on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at the Trivandrum City depot of an Indian state Kerala, for the period between 2010 and 2013. The forecasting performance of four time-series models is compared using Mean Absolute Percentage Error (MAPE) and the model goodness of fit is determined using information criteria. Conclusion: The forecasts indicate that multiplicative models with and without damping, which better account for seasonal variations, outperform the additive models.

Steering short-term demand for car-sharing: a mode choice and policy impact analysis by trip distance

Car-sharing could have substantial benefits. However, there is not enough evidence about if more people choosing car-sharing would reduce private car usage or public transport demand. This work aims to bring forward some insights by studying short-term car-sharing choice behavior. A mode choice analysis is conducted first followed by a simulation analysis to evaluate modal substitution pattern. Policy implications are obtained in terms of the possible measures that could effectively bring down private car usage. The case study is Taiyuan-China; stated and revealed preference data are collected. Mixed nested logit models are developed to study the pooled SP/RP data. The analysis is conducted separately for a shorter trip case (2–5 km) and a longer trip case (more than 5 km) to examine if results would differ by distance. It is found that raising the cost of private car usage (travel cost, parking cost) should be prioritized for shorter trips since car is more difficult to be substituted when trip distance increases. Shorter trips also need such direct measures to help suppress the demand for private car when promoting a car-sharing service; otherwise car-sharing would attract more bus users instead. Longer trips need a more effective solution to bring down private car usage and that is discovered as making car-sharing service more appealing so that it can serve as a practical substitute to private car. A number of informative indicators (e.g. willingness to pay for travel time savings, direct and cross point elasticity) are also derived to enrich the findings.

A dynamic line generation and vehicle scheduling method for airport bus line based on multi-source big travel data

Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi, ride-hailing service, subway, regular bus, airport bus, etc.), we accurately extract candidate stations, which are very popular with passengers and convenient for parking and transfer, through public transportation demand level calculation, iterative clustering and POI matching. Then, the candidate stations need to be partitioned appropriately by selecting suitable features and calculating the similarity of candidate stations, so as to make the stations within each group a moderate size and have a consistent spatial orientation. Finally, a line generation and vehicle scheduling algorithm, which is compatible with multi-vehicle, high success rate of ride-sharing matching and low cost, is designed to realize accurate and rapid operation scheduling within each group according to the situation of passengers booking tickets. We have carried out experiments in Wangjing and Yayuncun, and the results show that our method can satisfy passenger demand fast and accurately.

A soft traffic management approach for achieving environmentally sustainable and economically viable outcomes: An Australian case study

Transport infrastructural strain is a pressuring issue for the urbanised world due to the increasing demand for public transport. Transport operators, planners and policymakers are constantly searching for low-cost solutions to such transport issues. Therefore, it is critical to developing an environmentally sustainable and economically viable efficient traffic network to relieve traffic pressure (i.e., traffic congestion, transport infrastructure investment needs). The objective of this research is to propose a “Soft Traffic Management (STM)” concept to proactively analyse the traffic impact of transport planning strategy before implementation. This study investigates the effectiveness of a STM for easing the traffic pressure by carrying out a pilot research project on the proposed South East Busway extension in Logan City, Australia, by employing a stepwise process consisting of a multi-stage analysis and stakeholder-based modelling approach. The results indicate that the extended busway can significantly relieve traffic congestion. In addition, the proposed strategy has significantly positive impacts on the environment since it aims to reduce air pollution and fuel consumption as well as to improve the safety and efficiency of the whole transport system. This study confirms the effects of STM on improving the use of existing infrastructure more efficiently and deferring future transport infrastructure investments.

Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia

The demand for public transport and commuter behaviour are evidently affected by a series of D-factors relating to the built environment, including density, diversity, design, destination and/or demography. The effect of fare policies, however, in conjunction with built and non-built environment features has not been assessed. Given the 2017 fare policy change introduced in South East Queensland, Australia, this study examines how the policy reform (changes in fares, its structure and incentives) affects public transport ridership. Drawing on two like-for-like periods of transport smart card data before and after the policy reform, we compare the number of card users, journeys, and travel costs under the two fare systems. Through a set of statistical analysis and spatial lag regression, we examine the impact of the fare policy change on ridership, controlled by variations of built and non-built environment features, including population density, land use diversity, demographic features of commuters, distance to the central business district (CBD) and destination accessibility. Our findings show that public transit ridership can be boosted by reducing the fare cost per journey which can then result in overall revenue gain. However, such attraction by fare reduction varies substantially by user groups. Furthermore, the influences of population density, destination accessibility, distance to CBD and demographic features of commuters on ridership are significant (p < 0.01); while the influences of land use diversity and fare change tend to be insignificant compared to the other D-factors. We argue that in order to increase public transport usage policy makers need to consider fare policy reform in conjunction with built environment and demographic factors in order to increase service availability and ensure that services are accessible and affordable to the general public. This study also offers a generic framework that employs big data analytics to assess public policy intervention in the Australian context.

Out of the City Heat—Way to Less or More Sustainable Futures?

Rural alpine areas are affected by climate change in multiple ways. Today, many lower regions already face challenges in winter. However, several authors indicated new potentials for near-metropolitan areas in summer. As the first study for a metropolitan area, this paper discusses results of a large-scale quantitative survey (n = 877) from Vienna (Austria) to evaluate the intentions of urban residents to seek refreshment in nearby mountainous regions. The results regarding their adaptation behavior confirm the likely increase in demand to escape to nearby refreshing areas during heatwaves. This trend could lead to (re-)vitalization potential for rural near-metropolitan areas in Eastern Austria, which are often characterized by depopulation and degradation of infrastructure. A closer look at the respondents’ mobility behavior, reveals a high risk for unsustainable developments. Although the high and increasing share of car-free households in Vienna would suggest a strong demand for public transport, the likelihood to travel by car towards such destinations is high even among this group. Focusing predominantly on on-site mobility offers would be recommended since many travelers did not use their car within the destination. At last, the attractiveness of climate-friendly travel options is discussed considering mobility-related needs and preferences of three touristic motive groups.

Enhanced public transport management employing AI and anonymous data collection

The paper proposes a simple, economic and expandable solution for enhancing the data collection process used in public transport and transport demand management. A non-intrusive and anonymous method is employed to collect an estimative number of passengers in vehicles and public transport stops, along with other, relevant data. Machine learning and specific algorithms are used to improve the data collection process. No specific infrastructure equipment is required.

E-Hailing Transportation and the Issue of Competition in Indonesia

E-hailing transportation can be an alternative solution for both fulfilling the increasing demand for public transportation and reducing the number of vehicles on the road. As a populated country, Indonesia has benefited much from the emergence of e-hailing transportation. Unfortunately, despite positive impacts that have been enjoyed, numerous issues have arisen along with the growth of e-hailing transportation in the country. There are several indications that e-hailing companies have been involving in an unfair competition, including predatory pricing. This is done by offering very low fare of transportation services (commonly referred to as promotion fare) whose purpose is to eliminate their competitors. As such, the winner will be monopolizing the market and harming the ecosystem in it. The aims of this paper is to examine whether the Indonesian competition law can address the unfair business competition within e-hailing industry. It is found that e-hailing industry in Indonesia has been exposed to the practice of cash-burning by the business players. Competition law is needed to foster fair competition among the business players in e-hailing industry. Furthermore, the Government needs to formulate the good competition policy and ensure its enforcement.

Design of intelligent transportation systems for accessible transportation services

The demand for public transportation designed for people with mobility issues has grown rapidly in recent years, but service capacity has not increased at the pace of demand. Demands for major increases in service capacity have strengthened in recent years. In this paper, we study the design of an intelligent transportation system for accessible transportation services to enhance user experience and organisation operational efficiency. Our proposed system includes the components of data visualisation, real-time tracking via a mobile app and a vehicle scheduling optimisation mechanism. These three components are integrated in offline and online platforms. We illustrate the design and benefits of the proposed system through a case study.

A case study of a last-mile solution in a high-density residential neighborhood

Studies on new transportation services such as autonomous taxis and autonomous shuttles are underway in various cities around the world. Most of these studies discuss the effects (e.g., vehicle miles traveled, travel time, delay rate, and so on) of introducing new modes of transportation. This study analyzes the impact of providing autonomous minibus service in a dense residential area. Regular buses usually run along main roads with more than one lane, but minibuses can operate along roads with a single lane. There are many one-way roads around high-density apartments such as in Korean cities, so providing a last-mile solution can have a positive effect and increase the demand for public transportation. In addition, this study compared the level-of- service between different modes of transportation (e.g., car, walking, and minibus), unlike previous studies in which minibus demand was fixed. The results showed that minibuses did not replace the trunk traffic. These results suggested that minibus service should be used as an auxiliary for trunk traffic. According to the scenarios provided by the minibus, it was analyzed that the minibus service increased the public transport mode share by 3-5%.

Life-cycle Asset Management in Residential Developments Building on Transport System Critical Attributes via a Data-mining Algorithm

Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development’s public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents’ mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment.

Estimation of inter-city travel demand for public road transport in Nigeria

This paper models an intercity travel demand which uses the principal structural variables that have been identified in the literature. This research develops an urban public travel demand model for 19 directional O-D city-pair network originating from Owerri Urban. Using revealed preference data from the period of 2014 to 2016 which was filtered into 207 observations operated by the 16 transport companies in Owerri, Imo State. The empirical analysis explicitly modelled the pattern of correlations among service variables by a log-linear model using OLS. The estimates yield better demand elasticities than those of direct linear models. Other empirical findings include that; overall fares elasticities are low, so that increases in fare levels will almost always lead to increases in revenue. However, it is possible for any would-be operator determine the expected patronage on any route of operation using the derived models from this paper. Thus this paper has come up with empirical model to assess the viability of intercity passenger transport operation in Nigeria. However, it will also help the operators in business to do a sensitivity analysis based of changes in the intercity passenger travel markets in Nigeria.

Public transport accessibility tools matter: A case study of Gothenburg, Sweden

Urban transport investment decision-making has relied on traditional modelling tools that forecast travel demand based on existing travel patterns. This approach has also underpinned decisions about future urban development. Latent travel demand is poorly understood, and this is particularly important given policy aspirations for the take-up of more sustainable transport modes such as public transport. Accessibility tools can make an important contribution to future development and investment decisions given this policy aspiration. In this paper, public transport supply is assessed using an accessibility tool and then compared to travel patterns. By assessing both public transport supply and travel demand through an accessibility lens we are able to gain insight into the latent demand for public transport. An innovative segmentation type approach is employed by comparing supply and demand and from this a typology is proposed as a framework for a more holistic public transport investment strategy. This takes into account public transport accessibility, travel demand of trip makers and their socio-economic situation, set in the context of the urban region.

Real time transit demand prediction capturing station interactions and impact of special events

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.

Is Uber Helping or Hurting Mass Transit? An Empirical Investigation

Sharing economy reshapes the distribution of unused or underutilized asset through digital platforms to individuals who are willing to pay for the services. The new economy model has introduced dramatic impact on the traditional industries by matching the demand and supply in real time. In this paper, we examine how the entry of Uber, a ride-sharing services digital platform, influences the demand of public transportation. Significant debate has surrounded whether the new ride-sharing model siphoned riders from public transit, or made transit feasible for more riders. However, limited empirical research has been done to uncover whether Uber is helping or hurting public transportation systems. Leveraging a natural experiment setting, the entry of Uber, with a difference-in-differences approach, we find a significant drop in the number of passenger trips with bus. Further, our results suggest that the effect of Uber is not uniform – the reduction in bus passenger trips is mitigated when the proportion of old age people in a local urban area is large; the decrease is amplified when the populations of disable people, including with language difficulty, and self-employed people are large. However, the moderating effect of poverty is mixed. We discuss the implications for research and practice.

Improving Population Demand Estimation with Transit Chaining Breaks

Background: Data mining of smart card data collected through AFC systems have proved useful in estimations of public transport demand. Whereas most estimations of demand are made by analyzing transit orientations or destinations of unchained transits. However, organization of bus or metro routes compels riders to make a lot of unnecessary transfers, and the transfer points are neither reflective of population’s actual orientations nor of their destinations. Aims and Objectives: The objective of this paper is to improve estimations of population demand by identifying transfer activities of riders using public transportation. Durations and displacements of transit chaining breaks are to be check in judging the transfer activities. Boarding stops for making transfers are ruled out as transportation demand estimation. The effectiveness of the new approach entailing the use of transit chaining breaks is also to be evaluated based on the calculation of Pearson product-moment correlation coefficients for assessing the correlation between transportation estimation and population distribution. Result and Conclusion: Durations and displacements of transit chaining breaks could be used to identify transfer activities. The use of the transit chaining approach reduces the occurrence of false demand, resulting in the estimation being more objective in relation to the population. The results of the study indicated that the inclusion of transit chaining breaks leads to more accurate estimations of public transport demand within a population.

Analysing economic and environmental sustainability related to the use of battery and hydrogen energy storages for increasing the energy independence of small islands

Energy costs, carbon dioxide emissions, security of supply and system stability are common challenges in small islands. Many European islands have become pilot sites of energy innovation, but this green transition goes slowly in other ones usually not connected to the national grid. This study investigates the economic and environmental sustainability related to the integration of hydrogen and batteries storage in small islands, considering at the same time the use of the stored hydrogen for fuelling Fuel Cell Electric Vehicles and Hydrogen Compressed Natural Gas vehicles to meet electricity and public transportation demand of islands, so as to increase the Renewable Energy Sources penetration level. Selecting the island of Favignana (Italy) as case study, HOMER software has been used to carry out the energy analysis of different scenarios, in order to identify the most effective energy solution from both technological and economical point of views. Using economic and environmental indicators, the outcomes show that the implementation of a hybrid storage system with batteries and electrolyser can be an adequate and reliable option for increasing energy independency of small island and decarbonizing transport sector optimizing economic and environmental sustainability.