Public Transit Network Research Articles

Evaluating the accessibility benefits of the new BRT system during the COVID-19 pandemic in Winnipeg, Canada

Recently, in Winnipeg, the implementation of new bus rapid transit (BRT) system in the middle of the COVID-19 pandemic has raised many concerns, challenging the rationale behind the untimely release. However, the new BRT service can benefit low-income, socio-economically vulnerable, and transit captive passengers who must travel to essential services and work opportunities during the pandemic. This study evaluates whether the new BRT system has positive impacts on accessibility to such essential services during the pandemic. Isochrones with different time budgets as well as times of a day are generated based on high-resolution public transit network via the General Transit Feed Specification (GTFS) data and used for evaluating accessibility benefits before and after the BRT construction. The new BRT service in Winnipeg demonstrates varying accessibility impacts across different parts of the BRT corridor. Areas near dedicated lane-section show a significant increase, whereas areas near non-dedicated lane sections show a decrease in accessibility. Nevertheless, across the whole BRT corridor, the new BRT service presents an overall increase in accessibility to essential services. This demonstrates the positive accessibility benefits of the new BRT service to residents seeking essential services during the COVID-19 pandemic. A decrease in accessibility along some parts suggests the necessity of using local transit improvement strategies (e.g., dedicated lanes) to improve service speed when planning BRT services within urban areas.

Metro station risk classification based on smart card data: A case study in Beijing

As the hub of urban railway transit, metro stations portray the skeleton structure of the public transit network. This study proposes a method of station classification from the dual perspectives of network structure and passenger flow. Each perspective considers the two aspects, one is the characteristics of the node itself, such as degree and the entrance and exit ridership; another considers the characteristics of the influence of other nodes, such as betweenness centrality and passing flow. Among them, the importance index of passing flow is calculated by the PageRank algorithm. According to these characteristics, metro stations are classified by k-means clustering algorithm after dimensionality reduction. The case study is conducted through nearly five million records from 278 stations in Beijing. From the classification results, stations are divided into six categories. Qualitative and quantitative regulations are proposed to reduce the risk of high ridership stations and improve the operation efficiency for few ridership stations.

MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks

Many algorithms compute shortest-path queries in mere microseconds on continental-scale networks. Most solutions are, however, tailored to either road or public transit networks in isolation. To fully exploit the transportation infrastructure, multimodal algorithms are sought to compute shortest paths combining various modes of transportation. Nonetheless, current solutions still lack performance to efficiently handle interactive queries under realistic network conditions where traffic jams, public transit cancelations, or delays often occur. We present a multimodal separators–based algorithm (MUSE), a new multimodal algorithm based on graph separators to compute shortest travel time paths. It partitions the network into independent, smaller regions, enabling fast and scalable preprocessing. The partition is common to all modes and independent of traffic conditions so that the preprocessing is only executed once. MUSE relies on a state automaton that describes the sequence of modes to constrain the shortest path during the preprocessing and the online phase. The support of new sequences of mobility modes only requires the preprocessing of the cliques, independently for each partition. We also augment our algorithm with heuristics during the query phase to achieve further speedups with minimal effect on correctness. We provide experimental results on France’s multimodal network containing the pedestrian, road, bicycle, and public transit networks.

Exploring cascading reliability of multi-modal public transit network based on complex networks

Previous studies had focused on cascading failures of urban rail transit network (URTN) or bus transit network (BTN) while ignoring their coupling performances against cascading failures. Particularly, when considering the embedded effect of the other transit modes network (OTN, i.e., private cars and taxis sharing road infrastructures with BTN), cascading failures of multi-modal public transit network (MPTN) exhibit complex coupling dynamics caused by the intertwining and coupling effect of three types of failure load dynamic redistributions (FLDRs in a single layer or between layers). This work proposes a cascading reliability model to model, measure, and control this special type of cascading failures, which provides a novel perspective to test the entire network reliability with a unique advantage of calculation efficiency based on complex networks. Then, a case study concerning the cascading failures of an MPTN under impacts of a large passenger flow from a large-scale integrated passenger hub station is conducted. Results show that the proposed model has good adaptability, and its 5 / 6 control parameters have a different but effective correlation with cascading reliability and can realize the controllability of cascading reliability. Furthermore, the proposed multiple evaluation indicators of cascading reliability realize the diversity of measuring perspectives and demonstrate good mutual verification performances.

A Bi-Modal Simulation Model to Increase the Resilience of Public Transit Networks

A Bi-Modal Simulation Model to Increase the Resilience of Public Transit Networks

Queueing-Based Mathematical Formulation to Design a Public Transit Network Considering Energy Storage Technology and Demand Charges

Queueing-Based Mathematical Formulation to Design a Public Transit Network Considering Energy Storage Technology and Demand Charges

Research on Optimization of Urban Public Transport Network Based on Complex Network Theory

The urban public transportation system is an important part of urban transportation, and the rationality of public transportation routes layout plays a vital role in the transportation of the city. Improving the efficiency of public transportation can have a positive impact on the operation of the public transportation system. This paper uses complex network theory and the symmetry of the up and down bus routes and stations to establish an urban public transit network model and calculates the probability of passengers choosing different routes in the public transit network according to passenger travel impedance. Based on passenger travel impedance, travel path probability and passenger travel demand, the links are weighed, and the network efficiency calculation method is improved. Finally, the public transit network optimization model was established with network efficiency as the objective function and solved by the ant colony algorithm. In order to verify the effectiveness of the model and the solution method, this paper selects areas in Nanguan District of Changchun City for example analysis. The result shows that the efficiency of the optimized network is 8.5% higher than that of the original network, which proves the feasibility of the optimized model and solution method.

Topology analysis of Lanzhou public transport network based on double-layer complex network theory

The network topology is the basis of studying the overall performance and dynamic evolution mechanism of network. In regards to the study on the topology of multi-layer complex networks, the current studies fail to consider the heterogeneity of edges and nodes in multi-layer complex networks, and such methods are insufficient when they are used to analyze the static characteristics of topology. Therefore, this paper based on the theoretical basis of the multi-layer complex network, establishes a two-layer public transit complex network model where urban rail transit and bus transit cooperate with each other, and by optimizing this two-layer public transit complex network model, further proposes an integrated public transit network model. According to the method of analyzing the static statistical characteristics of complex networks, and considering the heterogeneity of nodes and edges in multi-layer network, this​ paper gives the evaluation indicators of static statistical characteristics of multi-layer public transit complex networks. Taking the two-layer public transit complex network structure in Lanzhou as an example, this paper finds out the main factors affecting its network stability and efficiency by analyzing the physical structure characteristics of this two-layer public transit complex network in Lanzhou where urban rail transit and bus transit cooperate with each other, and then puts forward the network optimization suggestions from the point of view of optimizing key nodes by using the method of identifying key nodes with centrality. In this study, the coupling among heterogeneous public transit network layers and the static structure characteristics of multi-layer public transit complex network are studied from the perspective of network structure, to assist the traffic management department in finding out the problems of the road network for the management and improvement of the performance and efficiency of the network. This has a profound influence on the development planning of urban public transit system.

WALK MODELLING WHEN DESIGNING SMALL BUS TRANSIT NETWORKS

Models for the design of public bus transit networks are usually based on the historically observed origin-demand travel patterns of potential passengers. The option of a passenger choosing to walk is, however, not usually incorporated into the design models for such networks. We argue that the absence of this model feature might lead to significant sub-optimality, especially in the design of small public bus transit networks. A novel approach to including this feature in the bus frequency setting aspect of public bus transit network design models is demonstrated in this paper, in which passenger transit modal choices are modelled on the basis of an existing optimal strategy transit assignment model. The newly proposed bi-objective route design and frequency setting models are applied to a realistic case study involving the establishment of a public bus transit system for students on a university campus; and this model implementation is used to gain insight into passenger route choice behaviour, based on individual preferences between walking or using buses. It is shown in the context of this case study that including the option of a passenger walking in the design models for public bus transit networks can affect the efficacy of the final network, especially if the network is small.

Partnerships in Urban Mobility: Incentive Mechanisms for Improving Public Transit Adoption

Problem definition: Because of a prolonged decline in public transit ridership over the last decade, transit agencies across the United States are in financial crisis. To entice commuters to travel by public transit instead of driving personal vehicles, municipal governments must address the “last-mile” problem by providing convenient and affordable transportation between a commuter’s home and a transit station. This challenge raises an important question: Is there a cost-effective mechanism that can improve public transit adoption by solving the last-mile problem? Academic/practical relevance: In this paper, we present and analyze two incentive mechanisms for increasing commuter adoption of public transit. In a direct mechanism, the government provides a subsidy to commuters who adopt a “mixed mode,” which involves combining public transit with hailing rides to/from a transit station. The government funds the subsidy by imposing congestion fees on personal vehicles entering the city center. In an indirect mechanism, instead of levying congestion fees, the government secures funding for the subsidy from the private sector. We examine the implications of both mechanisms on relevant stakeholders. These two mechanisms are especially relevant because several jurisdictions in the United States have begun piloting incentive programs, in which commuters receive subsidies for ride-hailing trips that begin or end at a transit station. Methodology: We present a game-theoretic model to capture the strategic interactions among five self-interested stakeholders (commuters, public transit agency, ride-hailing platform, municipal government, and local private enterprises). Results: By examining equilibrium outcomes, we obtain three key findings. First, we characterize how the optimal interventions associated with the direct or the indirect mechanism depend on: (a) the coverage level of the public transit network; (b) the public transit adoption target; and (c) the relative strength of commuter preferences between driving and taking public transit. Second, we show that the direct mechanism cannot be budget-neutral without undermining commuter welfare. However, when the public transit adoption target is not too aggressive, we find that the indirect mechanism can increase both commuter welfare and sales to the private-sector partner while remaining budget-neutral. Finally, we show that, although the indirect mechanism restricts the scope of government intervention (by eliminating the congestion fee), it can dominate the direct mechanism by leaving all stakeholders better off, especially when the adoption target is modest. Managerial implications: Our findings offer cost-effective prescriptions for improving urban mobility and public transit ridership.

Exploring the resilience assessment framework of urban road network for sustainable cities

Urban space for new transportation facilities cannot meet the increasing traffic demand. Afterward, scholars gradually increased attention to the resilience evaluation of urban road networks. Therefore, we proposed a resilience assessment framework of the urban road networks, including the resilience performance index, the robustness index, and the recovery index. Then we simulated the cascading failure based on a nonlinear load-capacity model with two capacity control parameters: α and β. Results show that the intersection-based attack has the most significant impact on resilience, and resilience is positively correlated with the node degree of the attacked intersection. The increase of α and β could enhance the resilience, and the urban road network achieves the best resilience performance when α=0.3,β=0.5. Compared with the deliberate attack strategy, the resilience performance under the random attack strategy is more robust. This research can provide the foundation for optimizing urban road networks and multi-mode urban public transit networks.

Reliability in public transit networks considering backup itineraries

Itinerary planning serves an important purpose for travelers navigating through a public transit network. The uncertainty of travel times within the network must be considered since this uncertainty can lead to delays or missed transfers. If an initial transit service or a transfer between services is missed, then the traveler must have a contingency plan for reaching their destination. However, finding reliable itineraries with contingency plans is computationally challenging. This paper outlines an a priori policy that maximizes the reliability, i.e. the probability of reaching the destination within a travel time budget, while considering the most reliable primary itinerary and backup itineraries in case a transfer is missed.We develop a network search algorithm that finds the most reliable itinerary with backups, which accurately accounts for the uncertainty of travel times and frequency of alternative options in the recommendation. We compare our approach to state-of-the-art contingency research, Dynamic Journeying Under Uncertainty by Häme and Hakula (2013), and show that it more accurately calculates the reliability, is more intuitive to the traveler, and has a faster runtime. Additionally, we highlight the differences in reliability across times of day, travel time budgets, and city regions for a real-world transit network in Göttingen, Germany. This approach offers travelers an easy-to-understand tool for planning reliable public transit travel itineraries.

Round-Based Public Transit Routing (Extended Abstract)

We study the problem of computing all Pareto-optimal journeys in a dynamic public transit network for two criteria: arrival time and number of transfers. Existing algorithms consider this as a graph problem, and solve it using variants of Dijkstra's algorithm. Unfortunately, this leads to either high query times or suboptimal solutions. We take a different approach. We introduce RAPTOR, our novel round-based public transit router. Unlike previous algorithms, it is not Dijkstra-based, looks at each route (such as a bus line) in the network at most once per round, and can be made even faster with simple pruning rules and parallelization using multiple cores. Because it does not rely on preprocessing, RAPTOR works in fully dynamic scenarios. Moreover, it can be easily extended to handle flexible departure times or arbitrary additional criteria, such as fare zones. When run on London's complex public transportation network, RAPTOR computes all Pareto-optimal journeys between two random locations an order of magnitude faster than previous approaches, which easily enables interactive applications. This is an extended abstract of the paper published at ALENEX 2012.

Extracting spatiotemporal commuting patterns from public transit data

Public transit networks in cities are crucial in addressing the transforming mobility needs of citizens for work, services and leisure. The rapid changes in urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations, modelling or population data that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variable demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of ≈4.6 million daily transit traces of the primary mode of underground services from the Greater London region reveals distinct commuting profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our results also suggest that heavily used stations that exhibit mixed-use commuting patterns are generally located in the cluster of the central business district and stations away from the centre of the city are largely single use residential areas. Overall, identifying mixed temporal and spatial use of stations diverging from macro mobility patterns in public transit indicates that our approach may be useful in a detailed understanding of integrated transit planning for heterogeneous needs of travellers.

Discussion on Optimization of Public Transportation Network Setting considering Three-State Reliability

In order to be environment-friendly, relieve traffic congestion, reduce pollution, and be green and sustainable, the optimization and development of public transportation, as the subject of people's long-term research, has always been shining. With the emergence of shared transportation, public transportation systems face more challenges. In order to better connect with bike-sharing, car-sharing, and other modes of transportation, public transportation will carry out important reforms, among which the optimization of line network is one of the most important tasks. The traditional bus route design is mainly based on the “four-stage” method model, which is mainly based on the investigation and analysis of the existing traffic system and land use. Through the work flow of “evaluation, calibration, and verification,” the network balance optimization model is used to get the bus travel allocation prediction model. In this paper, the optimization problem of public transit network is studied from the point of view of the reliability of public transit network. It is proposed that public transit network can be abstracted into series-parallel system and parallel-series system model from the three states of normal, short-circuit failure, and open-circuit failure and is analyzed and discussed through the hypothesis experiment. The research of this paper will provide a new perspective for the optimization of public transit network, complement the traditional methods, and support the optimization and reliability improvement of urban public transit network. More reliable bus networks and other modes of transportation, such as walking, bike-sharing, and rail, will become more suitable for people to get around.

Geospatially Partitioning Public Transit Networks for Open Data Publishing

Public transit operators often publish their open data in a data dump, but developers with limited computational resources may not have the means to process all this data efficiently. In our prior work we have shown that geospatially partitioning an operator’s network can improve query times for client-side route planning applications by a factor of 2.4. However, it remains unclear whether this works for all network types, or other kinds of applications. To answer these questions, we must evaluate the same method on more networks and analyze the effect of geospatial partitioning on each network separately. In this paper we process three networks in Belgium: (i) the national railways, (ii) the regional operator in Flanders, and (iii) the network of the city of Brussels, using both real and artificially generated query sets. Our findings show that on the regional network, we can make query processing 4 times more efficient, but we could not improve the performance over the city network by more than 12%. Both the network’s topography, and to a lesser extent how users interact with the network, determine how suitable the network is for partitioning. Thus, we come to a negative answer to our question: our method does not work equally well for all networks. Moreover, since the network’s topography is the main determining factor, we expect this finding to apply to other graph-based geospatial data, as well as other Link Traversal-based applications.

Understanding vehicles commuting pattern based on license plate recognition data

Commuting vehicles are one of the most important vehicles on the road network, especially during morning and evening peak hours. Analysis of commuting vehicles can lay foundation for policy formulation, urban planning, business decision-making. A large number of license plate recognition (LPR) detectors installed on the road network have accumulated a large amount of LPR data that contain the spatio-temporal information on vehicles. Therefore, using the LPR data, the commuting behavior of vehicles can be mined and analyzed. This study takes the LPR data of Hangzhou, China as an example, a series of algorithms are used to extract a total of nine features reflecting the commuting travel behavior using the one-month LPR data, and then the factor analysis method is used to integrate the nine extracted features into three factors. The commuting behavior is characterized from three perspectives: the stability of the vehicle travel behavior during the high frequency travel time period on working days, the stability of the vehicle travel behavior during the non high frequency travel time period, the stability of the origin and destination (OD) of a vehicle during the high frequency travel time period. Based on these three factors, three methods, including the density-based spatial clustering of applications with noise algorithm (DBSCAN), the iterative self-organizing data analysis technique algorithm (ISODATA), and the fast search and find of density peaks clustering algorithm, are used to identify commuting pattern vehicles and their results are compared. Finally, the decision tree algorithm is used to extract the commuting rule and identify commuting vehicles from the collected data. After that, The influence of different high frequency travel time period on commuting rule extraction and commuting pattern vehicle recognition is carefully analyzed. The influence of randomness on the results is also tested. The proposed commuting pattern vehicle recognition algorithm is proved to be robust. The detection frequency and the first and last detected times of commuting vehicles and non commuting vehicles are then analyzed. 78.0% of commuting vehicles were first detected between 06:30 AM and 10:30 AM on each workday on average. And 77.8% of commuting vehicles were last detected between 03:30 PM and 09:30 PM on each workday on average. However, for non commuting vehicles, the phenomenon that start or end the travel during the high frequency travel time period is not obvious. This study lays a foundation for travel policy formulation. For instance, it can provide a reference for the determination of the restricted periods when implementing the travel restriction policy. In addition, the identification of commuting vehicles is extremely useful for public transit network design and optimization, and it can help the decision-making process of a customized commuter bus. In addition, these strategies are expected to reduce the use of private cars and encourage travelers to switch to public transportation.

Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns

Studies on bike-share programs have dramatically increased during the past decades. While numerous studies have examined various factors affecting bike-share demand at the station-level, few attempts have been made to understand bike-share ridership at the origin-destination (OD) level due to technical difficulties. The objective of this study is to examine whether existing public transit characteristics affect bike-share ridership at OD-level. We combined three datasets: (1) bike-share ridership data, (2) land-use and bike-transit infrastructure, and (3) bike-transit route characteristics between OD pairs of bike stations. Zero-inflated negative binomial (ZINB) regression models were used for the analysis. Our results showed that the travel distance between OD bike stations, land-use compositions, and the existence of bike-friendly infrastructures were significant factors determining bike-share ridership at the OD-level. In particular, a longer duration of public transit trips than bike-share, and more transit transfers, were associated with bike-share ridership. Further, this study showed that bike-share and public transit might compete with or promote each other, even within the city. The study's findings suggest that the relative efficiency of bike-share compared to public transit is highly associated with bike-share demand and help to increase the utility of bike-share system in response to several limitations of existing public transit networks.

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.

Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption

This research analyzes the relationship between bike-sharing and public transit using bike-sharing data collected in Cologne, Germany. The selected system is one of very few in Germany that is organized as a free-floating system, which allows the generation of more detailed data. A construction site in the light rail network causing multiple disruptions in the public transit network offered the possibility to detect changes in bike-sharing usage that occur in the corresponding period. Applying negative binomial regression, spatial and temporal usage patterns are analyzed to identify connections to the public transit network and other factors influencing the usage of bike sharing. The analysis suggests the existence of a spatial relationship between bike-sharing and public transit. Therefore, an intermodal use of both means of transport can be assumed. The short-term changes in the public transit network caused by the construction site only have minor impacts on the usage patterns. Other factors that affect the usage structures could be identified. Proximity to universities as well as the number of certain points of interest nearby, such as food outlets and shops, promote bike-sharing use. Higher temperatures are also positively correlated, while rain reduces usage. The findings of the study can be beneficial to integrate bike-sharing into urban transport systems, especially regarding public transit.