The economics and engineering of bus stops: Spacing, design and congestion

This paper re-considers the problem of choosing the number of bus stops along urban routes, first by estimating the probability of stopping in low demand markets, and second by analysing the interplay between bus stop size, bus running speed, spacing and congestion in high demand markets. A comprehensive review of the theory and practice on the location and spacing of bus stops is presented. Using empirical data from Sydney we show that the widely used Poisson model overestimates the probability of stopping in an on-call bus stopping regime, and consequently underestimates the optimal number of bus stops that should be designed. For fixed-stop services, we show that bus running speed, frequency and dwell time are crucial to determining the relationship between bus stop spacing and demand, with bus stop congestion in the form of queuing delays playing a key role. In particular, we find that bus stop spacing should be decreased if demand increases at a constant bus running speed; however, if both bus running speed and the speed of the passenger boarding process increase, then the distance between bus stops should be kept long even at high demand levels, a result that is consistent with the implementation of Bus Rapid Transit systems that feature high bus running speeds and long distances between stops relative to conventional bus services

Multimodal pricing and the optimal design of bus services: new elements and extensions

This thesis analyses the pricing and design of urban transport systems; in particular the optimal design and efficient operation of bus services and the pricing of urban transport. Five main topics are addressed: (i) the influence of considering non-motorised travel alternatives (walking and cycling) in the estimation of optimal bus fares, (ii) the choice of a fare collection system and bus boarding policy, (iii) the influence of passengers’ crowding on bus operations and optimal supply levels, (iv) the optimal investment in road infrastructure for buses, which is attached to a target bus running speed and (v) the characterisation of bus congestion and its impact on bus operation and service design. Total cost minimisation and social welfare maximisation models are developed, which are complemented by the empirical estimation of bus travel times. As bus patronage increases, it is efficient to invest money in speeding up boarding and alighting times. Once on-board cash payment has been ruled out, allowing boarding at all doors is more important as a tool to reduce both users and operator costs than technological improvements on fare collection. The consideration of crowding externalities (in respect of both seating and standing) imposes a higher optimal bus fare, and consequently, a reduction of the optimal bus subsidy. Optimal bus frequency is quite sensitive to the assumptions regarding crowding costs, impact of buses on traffic congestion and congestion level in mixed-traffic roads. The existence of a crowding externality implies that buses should have as many seats as possible, up to a minimum area that must be left free of seats. Bus congestion in the form of queuing delays behind bus stops is estimated using simulation. The delay function depends on the bus frequency, bus size, number of berths and dwell time. Therefore, models that use flow measures (including frequency only or frequency plus traffic flow) as the only explanatory variables for bus congestion are incomplete. Disregarding bus congestion in the design of the service would yield greater frequencies than optimal when congestion is noticeable, i.e. for high demand. Finally, the optimal investment in road infrastructure for buses grows with the logarithm of demand; this result depends on the existence of a positive and linear relationship between investment in infrastructure and desired running speed

A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks

Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus-Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority

Bus scheduling with heterogeneous fleets:Formulation and hybrid metaheuristic algorithms

This paper focuses on optimizing mixed-fleet bus scheduling (MFBS) with vehicles of different sizes in public transport systems. We develop a novel mixed-integer nonlinear programming (MINLP) model to address the MFBS problem by optimizing vehicle assignment and dispatching programs. The model considers user costs, operator costs, and the crowding inconvenience of standing and sitting passengers. To tackle the complexity of the MFBS problem, we employ Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO). Besides, we develop two hybrid metaheuristics, including GA-SA [a combination of GA and Simulated Annealing (SA)] and GWO-SA (a combination of GWO and SA), to improve optimization capabilities for the MFBS problem. We also employ a Taguchi approach to fine-tune the metaheuristics’ parameters. We widely examine and compare the metaheuristics’ performance across various-sized samples (small, medium, and large), considering solution quality, computational time, and the result stability of each algorithm. We also compare the metaheuristics’ solutions with the optimal solutions acquired by GAMS software in small and medium-scale samples. Our findings show that the GWO-SA outperforms the other metaheuristics. Applying our model to a real bus corridor in Santiago, Chile, we find that precise dispatching plans generated by more sophisticated/advanced algorithms (GA-SA and GWO-SA) lead to larger cost savings and improved performance compared to simpler algorithms (GA and GWO). Interestingly, utilizing more advanced algorithms makes a difference in terms of fleet planning in crowded scenarios, whereas for low and medium-demand cases, simpler dispatching algorithms could be used without a drop in accuracy

Mobility-as-a-Service and the role of multimodality in the sustainability of urban mobility in developing and developed countries

Mobility as a service (MaaS) is an emerging framework that integrates multiple transport services into a single and intuitive platform. This paper contrasts the urban passenger transport markets in developed versus developing economies to understand the challenges of integrating mobility services using the MaaS framework, with a focus on decarbonization and sustainability as societal goals. In addition, we conducted a Life Cycle Assessment of carbon emissions and energy requirements of travel alternatives in the city of Santiago, Chile, to shed light on the effects of multimodality as an environmental tool. A summary of findings follows. Data sharing and open data are new in developing countries, and thus more investment in data infrastructure is required so that MaaS can leverage digital technology and network optimization. If the scalability of MaaS is an open question in developed countries, it is more so in developing countries, owning to institutional and financial constraints that are present in the latter. The lack of public subsidies to support formal public transport is a key limitation for the implementation of MaaS schemes and multimodal frameworks in the developing world. Regarding formality, in countries with an informal public transport sector, a potential implementation of MaaS will be spatially constrained to those locations where public transport operates formally and frequently (BRT and rail lines), limiting its spatial coverage and posing social equity issues. In countries with scarce or no public funds available for the transport sector, MaaS could be used as a catalyst for a broad environmental and equity-seeking transport pricing reform which requires a direct involvement of public sector in both regulation and financial backing. We conclude that the formalization and general improvement of the public transport sector, the regulation of shared-mobility platforms including the formalization of the work of drivers, and the setting of proper pricing and subsidization instruments in the direction of internalizing the social costs of motorized traffic, are all prerequisites for any MaaS system that aims to improve economic efficiency, social equity, and sustainability

Editorial: A better tomorrow: towards human-oriented, sustainable transportation systems

In a rapidly changing world, transportation is a big determinant of quality of life, financial growth and progress. New challenges (such as the emergence of the COVID-19 pandemic) and opportunities (such as the three revolutions of shared, electric and automated mobility) are expected to drastically change the future mobility landscape. Researchers, policy makers and practitioners are working hard to prepare for and shape the future of mobility that will maximize benefits. Adopting a human perspective as a guiding principle in this endeavor is expected to help prioritize the “right” needs as requirements. In this special issue, eight research papers outline ways in which transportation research can contribute to a better tomorrow. In this editorial, we position the research within the state-of-the-art, identify the needs for future research, and then outline how the included contributions fit in this puzzle. Naturally, the problem of sustainable future transportation systems is way too complicated to be covered with a single special issue. We thus conclude this editorial with a discussion about open questions and future research topics

Improving public transportation via line-based integration of on-demand ridepooling

Ride-sourcing companies have worsened congestion in numerous cities worldwide, as many users are attracted from more sustainable modes. To reverse this trend, it is crucial to leverage the technology of connecting users and vehicles online and use it to strengthen public transport, which can be achieved by integrating on-demand pooled services with existing fixed-line services. We propose an efficient and practical integration idea: namely, to complement fixed bus lines with a fleet of smaller vehicles that follow flexible (on-demand) routes side-by-side with the fixed routes, so that part of the demand that would have used the fixed line can ride the flexible service instead. With this scheme, a smaller bus fleet is required, partially compensating for the increase in operators’ costs stemming from the flexible vehicles. This integration strategy favors mostly two types of users: those traveling in low-demand periods, through lower waiting times, and those located far from the bus stops, because the on-demand vehicles can reduce their access time. We develop simulations in real-world scenarios from Santiago, Chile, and Berlin, Germany, for the cases of human-driven and automated vehicles. Results show that when vehicles are automated: (i) A small number of on-demand vehicles can reduce average walking times from approximately 12 to 2 min while reducing operators’ costs, leading to a Pareto improvement, (ii) A larger number of on-demand vehicles can diminish total costs by 13%–39%, through a reduction in users’ costs, although increasing operators’ costs. If vehicles are not automated, total costs are reduced by more than 10% in all of the scenarios analyzed, but a Pareto improvement is not always possible. In general, this mixed fixed/on-demand system outperforms the use of on-demand ridepooling only. Results are more promising in Berlin, because large buses are cheaper in Santiago and run more crowded, so it is more costly to partially replace them by smaller vehicles

Modelling Public Transport Corridors With Aggregate And Disaggregate Demand

Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Embedding risk attitudes in a scheduling model: Application to the study of commuting departure time

Traditionally, the value of travel time savings (VTTS) and the value of reliability (or reduced variability) are estimated within a linear utility functional form, which assumes risk-neutral attitudes for decision makers. In this paper, we develop non-linear scheduling models to address both risk attitude and preference in the context of a stated choice experiment of car commuters facing risky choices where the risk is associated with the trip time. We also investigate unobserved between-individual heterogeneity in time-related parameters and risk attitudes using a mixed multinomial logit (MMNL) model. More importantly, we calculate the willingness to pay values for reducing the mean travel time and variability (earlier/later than the preferred arrival time) within the non-linear scheduling framework. This model is then used to estimate preferred departure times for commuters, assuming that random link capacities are the source of travel time variability. Results show that the more variable travel times are, the earlier commuters depart, and that the non-linear scheduling model predicts earlier optimal departure times than the traditional linear scheduling model. Some important issues related to modelling non-linearity are also discussed