Search

Abstract Max‐weight (or max‐pressure) is a popular traffic signal control algorithm that has been demonstrated to be capable of optimising network‐level throughput. It is based on queue size measurements in the roads approaching an intersection. However, the inability of typical sensors to accurately measure the queue size results in noisy queue measurements, which may affect the controller's performance. A possible solution is to utilise the noisy max‐weight algorithm to achieve a throughput optimal solution; however, its application may lead to decreased controller performance. This article investigates two variants of max‐weight controllers, namely, acyclic and cyclic max‐weight (with and without noisy queue information) in simulated scenarios, by examining their impact on the throughput and environment. A detailed study of multiple pollutants, fuel consumption, and traffic conditions, which are proxied by a total social cost function, is presented to show the pros and cons of each controller. Simulation experiments, conducted for the Kamppi area in central Helsinki, Finland, show that the acyclic max‐weight controller outperforms a fixed time controller, particularly in avoiding congestion and reducing emissions in the network, while the cyclic max‐weight controller gives the best performance to accommodate maximum vehicles flowing in the network. The complementary positive characteristics motivated the authors to propose a new controller, herein called the hybrid max‐weight, which integrates the characteristics of both acyclic and cyclic max‐weight algorithms for providing better traffic load and performance through switching.

Cycling is a sustainable transport mode that endorses an active lifestyle. While cycling shows great potential, it is essential for urban planning to consider attributes influencing the choices that cyclists act upon. Cyclists’ route choices have been studied since the Eighties with knowledge being applied in cycling network planning. Yet, the role of cycling as a sustainable transportation mode has been largely absent from travel demand modelling. This paper researches cyclists’ route choice preferences and evaluates the opportunity of incorporating route choice modelling into travel demand modelling to improve the accuracy of cycling route choice. To this end, a route choice framework is developed in which a stated preference survey for data collection is conducted, a multinomial Logit model is applied to the data to identify the factors that significantly influence cyclists’ route choice behaviour. The generated route choice utility models are further integrated into an existing regional travel demand model to evaluate the performance of cyclists’ route choice modelling in the presence of additional factors. Then, the route choice model outputs are validated against two sets of external data. The results show that bike facilities, traffic volume, and trip length are the key factors influencing cyclists’ route choice preferences, and the generated route choice models can be an applicable improvement in incorporating the influences of cyclists’ preferences into travel demand modelling.

We present a computational study of network ensembles with two types of coexisting vehicle classes: an altruistically routing vehicle (ARV) class – potentially automated vehicles that are routed to reduce total system travel time – and a selfishly routing vehicle (SRV) class, corresponding to human-driven vehicles. We investigate the performance of these networks when some links are reserved for exclusive use by the ARVs. The goal of these interventions is to avoid or mitigate the detrimental effects of the SRVs on the costs of the ARVs. We formulate the problem as a bi-level network design problem, where the upper level deals with optimising the choice of ARV-exclusive links minimising the statistical dispersion of used-route costs, while the lower level finds the corresponding traffic equilibrium under static traffic assignment conditions. We tackle the ARV-exclusive link selection with a genetic algorithm, where the fitness of solutions is based on the dispersion of the costs of routes used by ARVs. The mixed equilibrium is found by solving a multi-class static traffic assignment problem, with constraints on the SRV flows on the ARV-exclusive links. SRVs attempt to minimise their personal travel time, whilst ARVs attempt to drive the flows to system optimal. Our approach is effective in reducing the per-vehicle travel cost of the ARVs to below that of the SRVs, making altruistic routing a more attractive option on average. Our results are consistent across networks with different structures and demand levels.

In the original publication [...]

Transit signal priority (TSP) is a traffic control strategy aiming at prioritising public transit vehicles at signalised intersections. The emergence of connected vehicles (CVs) provides the opportunity to enhance TSP operation, mitigating challenges such as the negative impact on nontransit users and the management of conflicting priority requests. Furthermore, traffic control policies produce environmental impacts, whilst TSP strategies are typically evaluated based on common traffic flow indicators, such as average vehicle speed, delay and/or the number of stops. In light of the recent progress made in CV technology, we propose and assess two user-based TSP strategies. The first approach aims to minimise total user delay at a signalised intersection, whilst the second considers both reducing bus schedule delay and total user delay. We also measure the environmental effects of these TSP strategies. A microscopic simulation environment is used to compare the proposed methods’ performance against a conventional TSP ring-and-barrier controller in a case study involving two adjacent signalised intersections in Helsinki, Finland. The findings indicate that implementing the proposed strategies effectively enhances TSP performance whilst also lowering adverse environmental impacts.

Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based models focused on behavioral theory, to properly frame the required detailed socio-economical characteristics. To address this challenge, this paper presents a novel Bayesian Optimization approach that incorporates a surrogate model defined as an improved Random Forest to automate the calibration process of the behavioral parameters. The presented solution calibrates the largest set of parameters yet, according to the literature, by combining state-of-the-art methods. To the best of the authors’ knowledge, this is the first work in which such a high dimensionality is tackled in sequential model-based algorithm configuration theory. The proposed method is tested in the city of Tallinn, Estonia, for which the calibration of 477 behavioral parameters is carried out. The calibration process results in a satisfactory performance for all the major indicators, the OD matrix average mismatch is equal to 15.92 vehicles per day while the error for the overall number of trips is equal to 4%.

Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy has mostly focused on wave dissipation without adequately assessing traffic efficiency and safety. To address this gap, an optimal control problem for JAD in mixed traffic is proposed to reduce traffic waves. The prediction model is developed using the car-following model within a model predictive control (MPC) framework. The Helly model is selected for the manual vehicle. This is because the Helly model is a linear model that describes the car-following phenomenon accurately without delay effect. In addition, the objective function of the prediction model considers both traffic safety and efficiency while satisfying mechanical and safety constraints. Simulation results indicate that the proposed methodology can effectively reduce traffic jams and improve traffic performance on a one-lane freeway. The optimal method is more applicable to complex traffic wave scenarios, providing a new perspective for reducing traffic jams on the freeway.

This study focuses on the transitioning period of operating mixed fleets of both automated and human-driven vehicles for public transit services. The type of service investigated here is flexible, including elements of both fixed route and on-demand systems. The operation of the mixed fleet is optimized with analytical methods leading to models for optimal service headway and stop spacing for the two types of vehicles. Analytical models for optimal passenger capacity per vehicle and required fleet size for each type of vehicles are also derived. Four operational strategies are considered, referring to whether the two types of vehicles operate jointly or independently in terms of optimal service headway and stop spacing within the mixed fleet. Numerical analyses indicate that automated vehicles operate optimally with less frequent vehicle dispatches and more fixed stop locations compared to human-driven vehicles. They also require greater fleet size and similar passenger capacity per vehicle. The four operational strategies perform similarly in terms of total generalized costs for the input values considered here. However, sensitivity analyses showed that the operational characteristics of the two types of vehicles in a mixed fleet and the performance of the four operational strategies depend significantly on the percentage of total demand that each type of vehicle serves, as well as on the automated vehicles’ speed and in-vehicle travel time cost for users. The mixed fleets represent the transitioning period towards transit fleets of automated vehicles only and it is shown to be the costliest period for both users and operators.

Abstract Connected vehicle (CV) technology is expected to bring additional opportunities to share, collect, and exploit various information on vehicles and their occupants. Assuming that CVs are able to transmit on‐board users and vehicle data, a user‐based signal timing optimisation (UBSTO) strategy is proposed, designed to optimise user throughput for signalised intersections. In the CV environment, the inputs of the proposed algorithm consist of position and speed of CVs, as well as the number of passengers travelling in each vehicle, while the output is the optimum green time duration for each signal phase. In addition, authors' proposed strategy is able to adapt the cycle length to the traffic volume condition. In case of missing users data, the same strategy can also operate in vehicle‐based mode, where the objective is vehicle‐throughput maximization. The performance of the proposed strategy is compared with a fully actuated controller (FAC) in microscopic simulation, for several scenarios, including different CV penetration rates. Authors' findings show that UBSTO can effectively increase user throughput and decrease average user delay in comparison with FAC, while also prioritising vehicles with higher number of users on‐board. These findings have implications for further development of prioritization strategies for public transport and ride‐sharing vehicles.

This paper presents a cooperative distributed model for the control of product flow in a network of cooperative inventory systems (ISs). In each IS, a linear input/output equation describes the balance between product demand vs production, as well as the product flows among ISs. The peculiarity of the problem is that both the production and demand are stochastic and cannot be controlled. In addition, each IS has just deterministic information on its inventory level (i.e., the quantity of stored product) which is not shared in the network. Other information is available in each IS regarding the forecast of both its related production and demand. The goal of the proposed model, on the overall network, is to keep both the product stored in each IS and the product exchanged among ISs, around given values as planned a priori. The only available control is related to the product flow between neighboring ISs, which has to be locally computed. Section II describes the proposed approach, which is based on the dual decomposition of the problem, which enables reaching the optimal control. This problem can represent the abstraction of a series of problems, which are not just related to logistics. As examples, section III presents two case studies: a network of virtual power utilities; and the control of risk in the transport of dangerous goods by road.

Evaluating potential of shifting to low-carbon transport modes requires considering limited travel-time budget of travelers. Despite previous studies focusing on time-relevant modal shift, there is a lack of integrated and transferable computational frameworks, which would use emerging smartphone-based high-resolution longitudinal travel datasets. This research explains and illustrates a computational framework for this purpose. The proposed framework compares observed trips with computed alternative trips and estimates the extent to which alternatives could reduce carbon emission without a significant increase in travel time.. The framework estimates potential of substituting observed car and public-transport trips with lower-carbon modes, evaluating parameters per individual traveler as well as for the whole city, from a set of temporal and spatial viewpoints. The illustrated parameters include the size and distribution of modal shifts, emission savings, and increased active-travel growth, as clustered by target mode, departure time, trip distance, and spatial coverage throughout the city. Parameters are also evaluated based on the frequently repeated trips. We evaluate usefulness of the method by analyzing door-to-door trips of a few hundred travelers, collected from smartphone traces in the Helsinki metropolitan area, Finland, during several months. The experiment’s preliminary results show that, for instance, on average, 20% of frequent car trips of each traveler have a low-carbon alternative, and if the preferred alternatives are chosen, about 8% of the carbon emissions could be saved. In addition, it is seen that the spatial potential of bike as an alternative is much more sporadic throughout the city compared to that of bus, which has relatively more trips from/to city center. With few changes, the method would be applicable to other cities, bringing possibly different quantitative results. In particular, having more thorough data from large number of participants could provide implications for transportation researchers and planners to identify groups or areas for promoting mode shift. Finally, we discuss the limitations and lessons learned, highlighting future research directions.

Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.