Bus Transit System Research Articles

Operational planning of public transit with economic and environmental goals: application to the Minneapolis–St. Paul bus system

This study develops a framework to optimize bus assignments and operating practices to routes considering both operating costs and environmental goals. The mixed-integer programming model is applied to the Metro Transit bus system in the Minneapolis–St. Paul metropolitan area. The model is used to derive representative solutions on the efficient frontiers between operating costs and emissions, and to demonstrate how economic factors such as fuel cost and service level affect the trade-offs between costs and environmental outcomes. An analysis of fleet composition shows that vehicle assignments can significantly affect the cost and emission performance of the fleet. We then use the model to evaluate the actual bus assignment schedule used by Metro Transit, and provide suggestions on how to reduce operating costs and emissions. The model is useful in supporting strategic decisions such as vehicle replacement and purchase, as well as operational planning.

Bus transit network design with uncertainties on the basis of a metro network: A two-step model framework

The metro systems of some megacities are facing serious oversaturation problem due to the heavy passenger flow during high peak hours. We consider the bus transit network design problem based on an existing metro network that can balance the modal split between metro and bus transit systems. The challenges facing this problem lie in that passengers have a different preference between metro and bus services, and the bus travel time and passenger demand may exhibit significant variations. This paper develops a two-step model framework to determine a bus transit network and departure frequency with consideration of travel time and passenger demand uncertainties. Firstly, we develop a column generation method to identify the candidate set of bus transit lines and passenger paths. Then a stochastic linear programming model is developed to optimize the bus line frequency and passenger path flow under demand and bus travel time uncertainty. To solve this model, a primal-dual online algorithm based on the online convex optimization theory is built to obtain the optimal solution with a theoretical performance guarantee. Finally, we implement the developed framework into an illustrative network and a real-world Beijing Second Ring public transit network to demonstrate its applicability and promising effects. The computational results show that the method can provide significant benefits for public transit systems.

Evaluating Route and Frequency Design of Bus Lines Based on Data Envelopment Analysis with Network Epsilon-Based Measures

Increasing the efficiency of bus transit remains to be a challenge of urban transportation. Since the optimization of bus routes and their frequencies is significant for transit efficiency, this study aims to develop two data envelopment analysis (DEA) models using network epsilon-based measures (NEBMs). The first NEBM model adopts twelve indicators to evaluate the rationality of the route network design; the second NEBM model uses nine indicators to evaluate the efficiency of the frequency setting. Both NEBM models can simultaneously consider radial and nonradial inputs and outputs and dig into details of the “input/output transformation box.” Finally, the bus transit system of Nanjing in China is used as a case study. Results show that the overall efficiency of network route design is higher than that of frequency setting. According to comparisons between descriptive statistics of the top and bottom performers, inefficiency causes of bottom performers are identified and corresponding improvement measures are suggested. The proposed models are helpful for the development of bus transit systems and can be applied on a yearly/monthly basis.

Alighting stop determination using two-step algorithms in bus transit systems

Smart cards of most bus transit system only record boarding stops of passengers, which hinders direct exaction of alighting stops. In this paper, we associate multi-source information to derive transit OD matrix. Passengers are segmented into regular and irregular groups using K-means clustering based on trip frequency. In the first step, a deterministic model such as the trip chain analysis is respectively conducted on the two groups of passengers. For records that fail to be detected by deterministic rules, machine learning algorithms are developed to determine the alighting stop in the second step. All the destinations can be detected by the two-step algorithms, while only partial records can be determined in the past. Transfer trip recognition helps to distinguish transfer trips from single trips. This research develops a set of simple and applicable methodologies in alighting stop determination, which can be applicable more broadly to other transit datasets.

Optimal Design of Bus Routes for Different Vehicle Types Considering Various Driving Regimes and Environmental Factors

As a major part of public transportation systems, bus transit has been regarded as an effective mode to alleviate traffic congestion and solve vehicle emission problems. The performance of a bus transit system depends largely on the design of bus stop locations. This research proposes a multi-period continuum model (peak and off-peak hours) to optimize the design of a bus route for four different vehicle types (i.e., supercharge bus, compressed natural gas (CNG) bus, lithium-ion battery bus, and diesel bus) considering driving regimes and pollutant cost. Inter-stop driving regimes—acceleration, cruising, coasting, and deceleration—are explicitly introduced into the optimization to determine whether and how the coasting regime should be undertaken in the tradeoff between commercial speed of vehicles and operating costs. The cost effectiveness of each alternative has been investigated in a life cycle and compared with respect to different vehicle types. The method has been applied to the real-world bus route no. 7 in Yaan City (China). The results of numerical experiments show that through optimization the total system cost can be reduced by more than 50%. The results of the continuum model are validated by comparison with the discretized results, and the outcomes are similar (with error less than 3%). Finally the life-cycle cost of the four vehicle types is analyzed, and the results indicate that, because of high purchase prices, it is difficult for clean-energy buses to outperform conventional buses in a life cycle (normally eight years), unless subsidies are provided.

Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty

This paper proposes a robust dynamic control mechanism for bus transit system, taking account of variations in congestion delays and passenger demand, and combines bus holding and operating speed control strategies. By using a prespecified uncertainty set, we propose a state space model for bus motion with delay disturbances and passenger demand uncertainties. According to the Lyapunov function analysis method, we design a robust dynamic control based on the state-feedback scheme as the bus control to achieve the robust stability of the bus transit system, which effectively reduces the bus bunching phenomenon. Furthermore, we formulate a nonlinear optimal control problem to design the robust optimal bus control, which not only reduces the bus bunching, but also improves the schedule adherence and headway regularity of bus service lines. To handle the complexity of the nonlinear optimal control problem with uncertain parameters and disturbances, we reduce it to a convex optimization problem by the minimization of an upper bound on the objective function. The problem is solved in a polynomial time and satisfies the practical real time requirement. Numerical examples are presented to validate the effectiveness of the model and control methods.

Climate co-benefits of alternate strategies for tourist transportation: The case of Murree Hills in Pakistan.

This study explores the climate impacts of on-road tourist transportation with alternate mitigation strategies. To this end, greenhouse gas (GHG) emissions for 2016 and emissions under four "what-if" scenarios were estimated for a popular tourist site in Pakistan, i.e., Murree Hills, using the international vehicle emissions model. Alternate scenarios included occupancy optimization, bus transit system, and Euro II and Euro IV implementation. The emissions were further decomposed using the log mean Divisia index method to study the drivers of global warming potential (GWP) mitigation. As per the results, the total 20-year GWP for 2016 was equal to 51,262 tons CO2 equivalent, and maximum reduction was achieved under the bus transit system scenario having a 20-year GWP of 25,736 tons CO2 equivalent, i.e., 49.8% reduction. Relative to the base year, GWP reductions were also quite significant for Euro IV (46.8%), Euro II (45.8%), and occupancy optimization (32.3%) scenarios. For the base year, CO2 held a share of 87.3% in total emissions; however, its share in the 20-year GWP was 39.7% indicating its reduced impact on total GWP as compared to N2O, CO, NOx, VOC, and CH4. Based on the decomposition results for alternate scenarios, GWP mitigation was mainly driven by CO, CH4, NOx, VOCs, and partially by CO2, while N2O negatively affected GWP mitigation. These results provide several policy-level instruments for developing countries to design a transition to an eco-friendly tourist transport management system. The policy implications from this study can be used to promote an eco-tourism industry.

Accounting for air pollution emissions and transport policy in the measurement of the efficiency and effectiveness of bus transits

ABSTRACTThis study presents a two-stage network performance evaluation model that determines the efficiency and effectiveness of bus transit systems. The model accounts for non-storable service features, undesirable outputs, and transport policy factors. The Taipei bus transit system is presented as an empirical case to measure the performance of bus transits with and without consideration of carbon dioxide (CO2) emissions and the transfer of riders. The results show that CO2 emissions and the transfer of riders are important factors for a performance evaluation model. Failure to include these results in an incomplete evaluation. Rather than focusing solely on marketable outputs, government agencies and managers must consider undesirable outputs or transport policy factors that relate to bus transits when measuring the efficiency and effectiveness of bus transits.

Reinforcement-Learning-Based Cooperative Adaptive Cruise Control of Buses in the Lincoln Tunnel Corridor with Time-Varying Topology

The exclusive bus lane (XBL) is one of the most popular bus transit systems in the U.S. The Lincoln Tunnel utilizes an XBL through the tunnel in the AM peak period. This paper proposes a novel data-driven cooperative adaptive cruise control (CACC) algorithm that aims to minimize a cost function for connected and autonomous buses along the XBL. Different from existing model-based CACC algorithms, the proposed approach employs the idea of reinforcement learning, which does not rely on accurate knowledge of bus dynamics. Considering a time-varying topology, where each autonomous vehicle can only receive information from preceding vehicles that are within its communication range, a distributed controller is learned real-time by online headway, velocity, and acceleration data collected from the system trajectories. The convergence of the proposed algorithm and the stability of the closed-loop system are rigorously analyzed. The effectiveness of the proposed approach is demonstrated using a well-calibrated Paramics microscopic traffic simulation model of the XBL corridor. The simulation results show that the travel time in the autonomous version of the XBL are close to the present day travel time even when the bus volume is increased by 30%.

A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system

This paper studies a multi-objective transit network design and frequency setting (TNDFS) problem with the aim of determining a set of routes and frequency on each route for public buses. The TNDFS problem is solved in two stages. First, the transit network design problem is solved to generate a set of routes using an initial route set generation (IRSG) procedure combined with genetic algorithm. Second, the frequency setting problem is formulated as a multi-objective model to assign the frequency on each route with the objectives of minimizing the passenger time and the operating cost simultaneously. This problem is solved using the multi-objective particle swarm optimization with multiple search strategies (MMOPSO) to generate a Pareto-Front between the passenger time and the operating cost. An extensive computational experiment is performed on the benchmark Mandl’s Swiss network (Mandl, 1980) considering different scenarios and instances. The results obtained using the proposed approach to solve the transit network design problem are compared with that of the state-of-the-art methodologies available in the literature (Arbex & Cunha, 2015; Kechagiopoulos & Beligiannis, 2014; Nikolić & Teodorović, 2013), which outperforms in terms of trip directness and average travel time of passengers. For the frequency setting problem, the solutions obtained using MMOPSO are justified over the NSGA-II.

Economic Assessment of Autonomous Electric Microtransit Vehicles

There is rapidly growing interest in autonomous electric vehicles due to their potential in improving safety, accessibility, and environmental outcomes. However, their market penetration rate is dependent on costs. Use of autonomous electric vehicles for shared-use mobility may improve their cost competitiveness. So far, most of the research has focused on the cost impact of autonomy on taxis and ridesourcing services. Singapore is planning for island-wide deployment of autonomous vehicles for both scheduled and on-demand services as part of their transit system in the year 2030. TUMCREATE developed an autonomous electric vehicle concept, a microtransit vehicle with 30-passenger capacity, which can complement the existing bus transit system. This study aims to determine the cost of autonomous electric microtransit vehicles and compare them to those of buses. A total cost of ownership (TCO) approach was used to compare the lifecycle costs. It was shown that although the acquisition costs of autonomous electric vehicles are higher than those of their conventional counterparts, they can reduce the TCO per passenger-km up to 75% and 60% compared to their conventional counterparts and buses, respectively.

Importance-Performance Analysis in Public Transportation: Methodological Revision for Practical Implementation

Importance-performance analysis (IPA) is a common and accepted method used by service industries to improve their service and therefore increase customer satisfaction. In public transportation, it has been adopted to assess the attributes of transit system. To analyze the practical implementation of IPA in transit user satisfaction studies, an extensive literature review was carried out to identify challenges. Unlike the “performance” dimension, measuring “importance” is challenging, given the wide variety of approaches and specific methods. For practical application, choosing a specific method in a specific approach has diverse implications. Real-world transit user satisfaction surveys of three different bus transit (sub) systems in Bogotá were used to try, test, and compare results, methods, applications, and—from a practical perspective—to identify the pitfalls, risks, difficulties, advantages, and biases of using different techniques to measure importance in relation to public transportation services. At the end of the paper, the advantages and shortcomings of different importance measurement methods are synthesized, and easy-to-implement suggestions for practitioners are provided.

Economic, environmental and congestion impact on the life-cycle cost of ownership: a case study in the Delhi transit bus system

Fast urbanisation and motorisation growth have resulted in degraded air quality and increased burden on most of the cities, globally. Cities utilise two-thirds of the global energy and account for about 70% of GHG emissions. Therefore, it is important to consider electric buses as an eco-friendly alternative to the conventional buses. To study financial and environmental impacts on the life cycle cost of ownership of conventional and electric buses, we have established the total cost of ownership (TCO) model by considering direct cost (acquisition and operation: owned by the fleet owner) and indirect cost (environmental damage and congestion: owned by the society). This study showed that EV's TCO would be economical, giving 30-40% advantage on fuel and maintenance. Further, EVs powered with renewable energy mix could prove to be a positive multiplier for socio-economic and environmental gain.

Economic, environmental and congestion impact on the life-cycle cost of ownership: a case study in the Delhi transit bus system

Economic, environmental and congestion impact on the life-cycle cost of ownership: a case study in the Delhi transit bus system

Assessing accessibility-based service effectiveness (ABSEV) and social equity for urban bus transit: A sustainability perspective

Service effectiveness (SEV) of bus transit indicates the impacts and quality of transit services. In any given city, not all residents enjoy the same level of accessibility to transit supply, with socially disadvantaged groups (SDGs) having more limited access. This study aims to measure the SEV of bus transit in terms of accessibility and investigate social equity in SEV among various population groups from a sustainability perspective. A systematic methodology is proposed integrating the cumulative opportunity measure and data envelopment analysis (DEA) in order to assess the accessibility-based service effectiveness (ABSEV) of bus transit, employing Lorenz curves and Gini coefficients to explore social equity in ABSEV. Using the elderly as an example of SDGs, an empirical application of Edmonton, Canada is provided. The results indicate that 18% of the total census tracts (CTs) perform effectively in ABSEV assessment of bus transit, while the remaining ineffective CTs are divided into three spatial clusters. For both seniors and the total population, Gini coefficients of ABSEV are under 0.5. The findings can provide policy makers with evidence on how to balance the ABSEV distribution of bus transit, improve benefits of bus transit to SDGs, and enhance sustainable development of urban bus transit systems.

Commuting Exposures to Particulate Air Pollution in Three Canadian Bus Transit Systems: The Urban Transportation Exposure Study

Commuting Exposures to Particulate Air Pollution in Three Canadian Bus Transit Systems: The Urban Transportation Exposure Study

Stakeholder Value Network: Modeling key relationships for advancing towards high quality bus transit systems

Most urban transportation projects are complex as they involve different areas of knowledge and must accommodate conflicting interests of many stakeholders. Although good practices in public transportation do exist, many cities in Brazil and around the world are not able to maintain these improvements due to strong direct and indirect pressures from key stakeholders. The lack of trust among stakeholders has been stronger than the trust between them. Understanding the relationships between stakeholders and evaluating the critical paths are key to facilitating the implementation of improvements. This paper develops a qualitative and quantitative model that represents the stakeholders' network involved in planning, operating and monitoring public bus transportation, and identifies key stakeholders and critical paths for decision-making. To demonstrate the application of the model, we performed a case study in Porto Alegre. We identified 13 key stakeholders directly and indirectly involved in the process. Through the Stakeholder Value Network methodology, it was possible to qualify and quantify 59 value flows between stakeholders that can influence the implementation of improvements in the public transportation system. The results provide useful information to facilitate improvements and in the services provided to the population.

Modeling and optimizing urban bus transit considering headway variation for cost and service reliability analysis

ABSTRACTDue to the stochastic nature of traffic conditions and demand fluctuations, it is a challenging task for operators to maintain reliable services, and passengers often suffer from longer travel times. A failure to consider this issue while planning bus services may lead to undesirable results, such as higher costs and a deterioration in level of service. Considering headway variation at route stops, this paper develops a mathematical model to optimize bus stops and dispatching headways that minimize total cost, consisting of both user and operator costs. A Genetic Algorithm is applied to search for a cost-effective solution in a real-world case study of a bus transit system, which improves service reliability in terms of a reduced coefficient of variation of headway.

Cost-Benefit Analysis of Developing a Light Rail Transit and Feeder Bus System in Utsunomiya City Considering the Change in Population Distribution

Cost-Benefit Analysis of Developing a Light Rail Transit and Feeder Bus System in Utsunomiya City Considering the Change in Population Distribution

Cost and fare estimation for the bus transit system of Santiago

This paper studies the costs of the transit system of Santiago, Chile, and proposes pricing schedules to finance it considering a fixed amount of subsidy. We estimate a cost function for the firms providing bus services in Santiago and study their industrial structure. We also estimate a demand model using aggregate data, which delivers information on the demand price elasticity and the effect of other variables related to the supply (e.g., capacity and frequency). Finally, we compute four pricing schedules: uniform price with and without subsidy and two-part tariffs with and without subsidy. Our results show that: (i) the industry exhibits economies of scale when considering both demand-oriented and supply-oriented output measures; (ii) measures of technical efficiency are consistent with the observed exit of firms from the market; (iii) the budget-balanced fare without subsidy is higher than the actual fare, suggesting that subsidies are justified; (iv) the budget-balanced fare with subsidy is higher than the actual fare, suggesting that subsidies are insufficient; (v) two-part tariffs either with or without subsidy are the most effective means of increasing bus travel demand and user welfare; and (vi) a menu of tickets with two options (a uniform price and a two-part tariff) is a Pareto-improving pricing schedule that balances the budget and increases bus travel demand.