Bus Transit Network Research Articles

On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.

The impacts of optimization approaches on BEB system configuration in transit

Battery-electric buses (BEBs) are considered suitable technology for transit to tackle climate change and promote environmentally friendly mobility solutions. However, the systemic configuration of BEBs in transit requires sophisticated planning efforts due to contradictory objectives and decisions. The optimal design of a BEB transit system is often approached from various perspectives, leading to different system configurations and distinct impacts on the electricity grid. Towards that end, this study develops three BEB system configuration optimization models, including minimizing capital costs, electricity costs, and greenhouse gas (GHG) emissions. All three models inform the optimal charging system configuration, BEBs battery capacity, and BEBs charging schedule for a general hub-and-spoke transit network. The proposed models are applied to a case study of the Belleville City, Ontario, Canada, bus transit network. The results demonstrate that BEB system configuration and GHG emissions vary significantly according to the optimization perspective. Moreover, the findings emphasize the importance of using the energy storage system to reduce electricity costs and GHG emissions.

A Bus Network Design Model under Demand Variation: A Case Study of the Management of Rome’s Bus Network

This paper proposed a methodology to design bus transit networks that can be consistently adjusted according to demand variations both in level and distribution. The methodology aims to support the activities of service providers in optimizing the service capacity of the bus network according to a system-wide analysis. It stems from the changes imposed by the COVID-19 pandemic. Such an experience has imposed a rethinking of the methodology used for the optimal design of robust transit network services that are easy-to-adapt to demand variations without redesigning the whole network every time. Starting from an existing model, this design methodology is articulated in two parts: the first part for solving the problem with the maximum level of transit demand, aiming at giving an upper bound to the solution, and the second part, where the network is optimized for other specific transit demands. This method has been applied to a real context in the city of Rome, considering two levels of demand taken from COVID-19 experiences. They are characterized by the application of different policies regarding different timings for shopping and schools’ openings as well as by policies on smart working. The results show the effectiveness of the proposed methodology to design robust transit networks suited to comply with large demand variations. Moreover, the procedure is suitable and easy to implement, in order to adapt quickly to changes in demand without having to modify line routes, but adapting them in an optimal way, even when dealing with realistic-sized transit networks.

A critical review of analytical approaches in public bus transit network design and operations planning with focus on emerging technologies and sustainability

The Public Bus Transit Network Design and Operations Planning Problem (PBTNDOPP) is a complex transportation problem. Analytical approaches are one of the key approaches to studying this problem, often leading to optimal or near-optimal solutions with reduced computational complexity compared to mathematical programming. This article leverages the Critical Path Method (CPM) to visualize the historical applications of analytical approaches in PBTNDOPP. Then, it reviews the applications of these approaches to some of the recently emerged sub-problems identified via CPM, i.e., sustainable public bus transit network design and introducing emerging transportation technologies to these systems. Our review aims to shed light on the current state of the literature and its future direction in the selected sub-problems by analyzing published studies in the last decade from various angles, such as the problems investigated, modeling methods, decision variables, network structures, and findings. The review shows that the existing body of literature on the application of analytical approaches to the selected problems is immature and at an early stage of development. For example, most of the studies on sustainable PBTNDPPP have not included all dimensions of sustainability and have only focused on environmental sustainability, while considering social criteria such as fairness and equity in PBTNDOPP is crucial for designing a sustainable PBTS. Another important gap is related to hybrid methods based on analytical approaches that take realistic assumptions and uncertainty in various problem parameters and variables into account.

Vulnerability analysis of public transit systems from the perspective of the traffic situation

The critical to sustainable urban development that a robust bus transit system is constructed to alleviate urban congestion problems. Unfortunately, disruptions in bus transit systems can lead to dysfunction and threaten sustainable development. This paper proposed a vulnerability analysis model for constructing complementary urban bus networks (UBNs), which considers the actual capacity of the bus network at the speed of transport roads. In addition, the UBNs aimed analysis method is presented from three specific aspects: (a) utilizing the stochastic user equilibrium model and different traffic situations to measure the UBNs of passenger flow distribution according to multi-source data; (b) introducing the node capacity and structural, functional vulnerability analysis model by combining real traffic flows and the complex network theory; (c) analysing the vulnerability characteristics of UBNs cascading failures from different passenger flow distributions and increasing capacity parameters, and comparing with those under different attack methods. Then, the new vulnerability analysis model is applied to the Harbin trunk bus. Considering different attack scenarios, the UBNs show strong vulnerability in the case of high congestion. Compared with other deliberate attacks, the bus network based on the attack mode of the maximum intensity site is more vulnerable at different times. These insights could be used to inform a vulnerability-based spatiotemporal design of bus transit networks.

Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network

With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications.

Resilience assessment framework toward interdependent bus–rail transit network: Structure, critical components, and coupling mechanism

Understanding the interdependent nature of multimodal public transit networks (PTNs) is vital for ensuring the resilience and robustness of transportation systems. However, previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs, neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal PTNs. Therefore, this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism, structural and functional characteristics of bus–rail transit networks (BRTNs). In this framework, a network performance metric is proposed by considering the passengers’ travel behaviors under various disturbances. Additionally, stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or subnetwork. The proposed framework is validated via a case study of a BRTN in Beijing, China. Results indicate that the rail transit network (RTN) plays a crucial role in maintaining network function and resisting external disturbances in the interdependent BRTN. Furthermore, the coupling interactions between the RTN and bus transit network (BTN) exhibit distinct characteristics under infrastructure component disruption and functional disruption. These findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

Evaluating fare evasion risk in bus transit networks

In Proof-of-Payment Transit Systems (POP-TSs), fare evasion is a crucial issue for Transit Agencies (TAs) and/or Public Transport Companies (PTCs) worldwide. The related research background is based on the standard ratio between evaders and inspected passengers, whereas no research quantified the risk of fare evasion in POP-TSs. The objective of this study is the introduction of a framework covering this gap: it integrates fare evasion factors, prediction models, a risk-based method and returns the risk value on (parts of) routes as a function of the frequency of fare evasion, the related severity and exposure terms. Next, routes are ranked according to the risk value and classified by a 5-level scale, to show the (parts of) routes with the highest risk of evasion. Results show the capability of this framework on about 20,000 real-world data records gathered by a mid-sized Italian bus company through fare inspection logs and passenger surveys. To conclude, this framework is a support tool for TAs/PTCs to improve fare compliance and can be incorporated into any transit managerial system.

An open-source program for spatial decomposition of bus transit networks

Comparing transit operations and performance over time is often challenging due to changes in route identifiers and service patterns. It is particularly difficult if the analysis period spans a major network design change where bus routes are realigned and renamed. This paper presents a new open-source software package that simplifies longitudinal transit performance analysis by decomposing a bus network into block-length segments that remain stable over time. The package then matches transit networks across time periods using segment geometry rather than potentially unstable route or stop identifiers. This package provides an efficient method for transit planners, advocacy groups, policy makers and urban scientists to analyze changes in transit performance over time without any prior knowledge of network design changes.

Transit Behaviour and Sociodemographic Interrelation: Enhancing Urban Public-Transport Solutions

Recent years have seen a considerable shift in the focus of public investment agencies from extensive roadway networks to a more planned approach that meets environmental, cost, and social dimensions more aptly. Past research has mainly explored the engineering aspect and cost parameters, while the human or social component is often neglected. This study aims to identify the trip-making behaviour of residents in an urban area towards bus transport network enhancement. Abu Dhabi, the location of study, is heavily dependent upon car travel, creating much congestion, which the local government seeks to address by enhanced public transport. This work examined eight public-transport routes in two zones, with data collected on both weekdays (n = 751) and weekends (n = 769). Multinomial logistic regression models showed that respondents highlighted overcrowded buses and traffic congestion as two of the main hurdles pertinent to urban routes in the bus network influencing their mode choice. Proposals pertinent to the local authority for further consideration need to factor in current low satisfaction with bus transit network coverage, low satisfaction with the quality of bus rides, inhibiting a mode shift from cars/taxis towards buses, cumulative income profiles of public-transport users, with findings that the low-income bracket is already at saturation, and that reducing congestion needs innovative (sociodynamic rather than technical road network) public-transport solutions.

Optimal headway-based bus dispatching strategy under the influence of epidemic outbreaks

This study introduces a bi-level model to help identify the optimal headway-based dispatching strategy for bus transit network operations under the influence of epidemic outbreaks. Specifically, the optimal route-specific bus dispatching headways are sought to minimize the weighted total cost of travel time of all network travelers and medical treatments for newly infected travelers. Heuristic algorithms are developed to iteratively execute the proposed lower- and upper- level models in succession until convergence. Computational experiments are conducted for a 4-km urban bus transit corridor and a 7-square-km central business district (CBD) bus transit network, respectively. Without vaccination coverage, the total travel cost is expected to increase for higher infection rates. Implementing the optimal headway-based bus dispatching strategy could always reduce the total travel cost. However, mandating social distancing rules is largely more effective and a larger social distance becomes essential for higher infection rates. When the vaccination coverage reaches certain threshold levels such as 70% or higher, infection risks could be mitigated by the optimal headway-based bus dispatching strategy where no social distance becomes necessary. Model enhancements could be made to handle temporal and spatial variabilities of epidemic impacts and susceptible travelers.

The Robustness of Battery Electric Bus Transit Networks under Charging Infrastructure Disruptions

The optimization of battery electric buses (BEBs) systems in transit is receiving considerable scholarly and practical attention. The practice is to minimize the total system cost to inform the optimal resource allocation. However, a minimization approach is insensitive to assessing and accommodating the robustness of BEB transit systems under disruption. This study evaluates the robustness of the BEB transit system under charging infrastructure disruption using complex network theory. The results of a mid-size multi-hub network indicate that the BEB system is robust against disruption if the disruption is resolved in a timely manner (within one hour). Furthermore, multi-charger charging stations have severe impacts on the system’s robustness. Overall, the BEB system robustness is more sensitive to the hourly number of buses charging at each station and the duration of the charging events.

A Variable Neighbourhood Search-Based Algorithm for the Transit Route Network Design Problem

The transit route network design problem (TRNDP) has long attracted research attention, with many metaheuristic approaches proposed for its solution. So far, and despite the promising performance of Variable Neighbourhood Search (VNS) variants for vehicle routing problems, the performance of the algorithm on the TRNDP remains unexplored. In this context, this study develops a VNS-based algorithm for the problem at hand. The performance of the algorithm is tested using benchmark networks used in bus transit network design and compared with some of the most recent and efficient methods from the literature. Results show that the algorithm yields superior results over existing implementations in short computational times.

A direct demand model for bus transit ridership in Bengaluru, India

• A disaggregate direct demand model of bus transit ridership in Bengaluru, India. • Log-sum variable as an empirical strategy to relate spatially aggregated demand data to disaggregate stop-level catchment area characteristics. • Route-level service frequency is endogenous to and has a non-linear impact on stop-level ridership. • Inter-route relationships (competition and complementarity among routes) within the bus network and with the Metro network significantly influence bus ridership. • The proposed framework and empirical strategies are applicable to analyze transit ridership in other cities. This study formulates a disaggregate direct demand model of bus transit ridership while addressing the following substantive and methodological issues: (a) endogeneity and non-linearity of the influence of service frequency on ridership, (b) inter-route relationships such as competition and complementarity among routes within the bus transit network and with other transit networks (such as the metro/rail network), (c) relating spatially aggregated demand to disaggregate, stop-level catchment characteristics – although demand data are available only at an aggregation of stop-clusters, and (d) overlapping of catchment areas among closely spaced stops. The proposed model is applied to analyze bus transit ridership (boardings) during weekdays for morning peak period in Bengaluru, India. This study is among the first to develop a comprehensive direct demand model for forecasting bus transit ridership in an Indian city. Yet, the proposed conceptual and methodological framework and the findings from the study are general enough to be of use for transit planning in other cities of India and other countries. Transit agencies with spatially aggregate, fare-stage cluster-level ridership data can employ the proposed approach to examine the influence of disaggregate stop-level catchment characteristics on ridership. Additionally, transit agencies may utilise the proposed model to quantify bus ridership impacts of service network modifications, route alignments, and network connectivity/accessibility, while considering interactions with other transit networks. The empirical results suggest that while increasing service frequency increases ridership along low-frequency routes, the returns from increasing service frequency diminish as current frequency levels increase. Further, it is shown that route-level passenger kilometres, a variable commonly available with transit agencies, serves effectively as an instrument for addressing endogeneity between route-level service frequency and stop-route-level ridership.

Temporal robustness assessment framework for city-scale bus transit networks

Despite the prevalence of bus transit networks in urban public transportation, they are very susceptible to disruptions that can have negative implications, accompanied by severe socioeconomic consequences and service quality consequences. In addition, since bus transit system is comprised of stops, routes, and passengers, any disruptions in the bus network can trigger chain reaction disruption scenarios, leading to cascade failures across the entire network. Unlike the existing literature, the present study evaluates the robustness of bus transit network considering the varied hourly operation. The robustness assessment is evaluated using a weighted network considering losses in bus frequency and passenger flow, both are essential to inform service quality improvement. Some of the key findings include the observed significant variation in the hourly performance of the network compared to the daily performance when a failure occurs. Moreover, the network is more robust during on-peak hours and mid-peak compared to off-peak hours. However, the network is sensitive to losing service frequency compared passenger flow when a failure happens. These insights could be used to inform a robustness-based spatiotemporal design of bus transit networks.

Dynamic Surrogate Trip-Level Energy Model for Electric Bus Transit System Optimization

The introduction of electric mobility solutions mitigates transportion-related greenhouse gas emissions. Transit buses are considered a promising candidate for electrification. This study contributes to the growing literature on battery-electric buses (BEBs) and aims to quantify the optimal allocation of BEB infrastructure and charging schedules. A generic model for the charging capacity and scheduling of the BEB network is developed. The proposed model proposes an algorithm for the calculation of trip-level BEB energy consumption based on a surrogate model-based space mapping algorithm. Instead of using vehicle simulators or constant values for the energy consumption rate for each trip, the input space mapping has been applied to a simple coarse model to build an accurate surrogate model. The proposed algorithm is tested on the bus transit network in Belleville City in Canada considering BEBs using both Flash and Opportunity charging. The results show the efficiency of the proposed model and highlight the impact on the optimization results of calculating the trip-level energy consumption compared with the traditional methods.

Exploring vulnerability bottlenecks of large-scale bus transit networks based on topological dynamics

Exploring vulnerability bottlenecks of large-scale bus transit networks based on topological dynamics

Assessment of the Bus Transit Network: A Perspective from the Daily Activity-Travel Organization of Travelers

In a context where daily car use is a spontaneous and habitual choice for a wide majority of the population, the quality of the alternatives to individual motorized vehicles is a major factor in encouraging modal shift. The ease of access to public transport can influence the mode choice decision process. However, the diversity of activity-travel patterns questions the definition of a unique and homogeneous accessibility and level-of-service to all travelers. The aim of this paper is to identify, with the help of standard data (micro-census and open data), for whom, when and where the transit supply is adequate or not. The approach is based on a twofold methodology. First, we aim to identify differentiated typical activity-travel patterns among the population. Second, to decompose them in transit supply in time and space. Combined, these two elements synthesize a spatially- and temporally-based demand-supply gap index. The results show for which territories, time slots and population groups the supply is defective or excessive with respect to the demand. Generally speaking, the supply is particularly well dimensioned for the dominant groups, such as commuters, including long-distance travelers, who are mainly men. The imbalances in supply over time and space reveal a differentiated accessibility but also significant socio-spatial inequalities.

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.

Pathways to achieve future CO2 emission reduction targets for bus transit networks

Apart from electric vehicles, emissions targets for 2025 and 2030 in the heavy-duty transportation sector could be achieved with hybrid powertrains. Moreover, alternatives such as the use of synthetic or e-fuels may also offer a feasible path for transport decarbonization. This work explores different pathways to reduce CO₂ emissions considering the city of Valencia as a case study. The 10 most used bus lines operating in the city are evaluated using their GPS based vehicle speed information with 0D GT Suite simulations. First, the hybridization level for the share of buses was varied from 0 to 100% and the number of different bus types operating in each line was optimised for minimum CO2. Next, the battery and E-motor sizing is optimised for each bus line. Further, an assessment was done assuming 100% electrified fleet, with the 2030 and 2050 electricity generation CO2 footprint projections. Moreover, the potential of e-fuels in the current fleet is also evaluated. The results show that to meet the 2050 target, 100% electrified fleet (with 2050 electricity mix) as well as using e-fuels (generated from renewable sources) in the current fleet are feasible options. However, the e-fuel pathway is more economical than 100% electric fleet.