Dynamic user equilibrium in public transport networks with passenger congestion and hyperpaths

This paper presents a dynamic user equilibrium for bus networks where recurrent overcrowding results in queues at stops. The route-choice model embedded in the dynamic assignment explicitly considers common lines and strategies with alternative routes. As such, the shortest hyperpath problem is extended to a dynamic scenario with capacity constraints where the diversion probabilities depend on the time at which the stop is reached and on the expected congestion level at that time. In order to reproduce congestion for all the lines sharing a stop, the Bottleneck Queue Model with time-varying exit capacity, introduced in Meschini et al. (2007), is extended. The above is applied to separate queues for each line in order to satisfy the First-In-First-Out principle within every attractive set, while allowing overtaking among passengers with different attractive sets but queuing single file. The application of the proposed model to a small example network clearly reproduces the formation and dispersion of passenger queues due to capacity constraints and thus motivates the implementation of the methodology on a real-size network case as the next step for future research

Strategy-based dynamic assignment in transit networks with passenger queues

This thesis develops a mathematical framework to solve the problem of dynamic assignment in densely connected public transport (or transit – the two words are interchangeably used) networks where users do not time their arrival at a stop with the lines’ timetable (if any is published). In the literature there is a fairly broad agreement that, in such transport systems, passengers would not select the single best itinerary available, but would choose a travel strategy, namely a bundle of partially overlapping itineraries diverging at stops along different lines. Then, they would follow a specific path depending on what line arrives first at the stop. From a graph-theory point of view, this route-choice behaviour is modelled as the search for the shortest hyperpath (namely an acyclic sub-graph which includes partially overlapping single paths) to the destination in the hypergraph that describes the transit network. In this thesis, the hyperpath paradigm is extended to model route choice in a dynamic context, where users might be prevented from boarding the lines of their choice because of capacity constraints. More specifically, if the supplied capacity is insufficient to accommodate the travel demand, it is assumed that passenger congestion leads to the formation of passenger First In, First Out (FIFO) queues at stops and that, in the context of commuting trips, passengers have a good estimate of the expected number of vehicle passages of the same line that they must let go before being able to board. By embedding the proposed demand model in a fully dynamic assignment model for transit networks, this thesis also fills in the gap currently existing in the realm of strategy-based transit assignment, where – so far – models that employ the FIFO queuing mechanism have proved to be very complex, and a theoretical framework for reproducing the dynamic build-up and dissipation of queues is still missing.Open Acces

Capacity constrained accessibility of high-speed rail

This paper proposes an enhanced measure of accessibility that explicitly considers circumstances in which the capacity of the transport infrastructure is limited. Under these circumstances, passengers may suffer longer waiting times, resulting in the delay or cancellation of trips. Without considering capacity constraints, the standard measure overestimates the accessibility contribution of transport infrastructure. We estimate the expected waiting time and the probability of forgoing trips based on the M/GB/1 type of queuing and discrete-event simulation, and formally incorporate the impacts of capacity constraints into a new measure: capacity constrained accessibility (CCA). To illustrate the differences between CCA and standard measures of accessibility, this paper estimates the accessibility change in the Beijing–Tianjin corridor due to the Beijing–Tianjin intercity high-speed railway (BTIHSR). We simulate and compare the CCA and standard measures in five queuing scenarios with varying demand patterns and service headway assumptions. The results show that (1) under low system loads condition, CCA is compatible with and absorbs the standard measure as a special case; (2) when demand increases and approaches capacity, CCA declines significantly; in two quasi-real scenarios, the standard measure overestimates the accessibility improvement by 14–30 % relative to the CCA; and (3) under the scenario with very high demand and an unreliable timetable, the CCA is almost reduced to the pre-BTIHSR level. Because the new CCA measure effectively incorporates the impact of capacity constraints, it is responsive to different arrival rules, service distributions, and system loads, and therefore provides a more realistic representation of accessibility change than the standard measure

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

Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System

With the growing problem of urban traffic congestion, departure time choice is becoming a more important factor to commuters. By using multiagent modeling and the Bush-Mosteller reinforcement learning model, we simulated the day-to-day evolution of commuters’ departure time choice on a many-to-one mass transit system during the morning peak period. To start with, we verified the model by comparison with traditional analytical methods. Then the formation process of departure time equilibrium is investigated additionally. Seeing the validity of the model, some initial assumptions were relaxed and two groups of experiments were carried out considering commuters’ heterogeneity and memory limitations. The results showed that heterogeneous commuters’ departure time distribution is broader and has a lower peak at equilibrium and different people behave in different pattern. When each commuter has a limited memory, some fluctuations exist in the evolutionary dynamics of the system, and hence an ideal equilibrium can hardly be reached. This research is helpful in acquiring a better understanding of commuter’s departure time choice and commuting equilibrium of the peak period; the approach also provides an effective way to explore the formation and evolution of complicated traffic phenomena

Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set