Automated vehicles and the city of tomorrow: A backcasting approach

The introduction of Autonomous Vehicles (AVs) into cities may fundamentally transform the design and use of cities. On one hand, AVs offer the potential to reduce the urban space requirements for roads and parking, creating more space for high-quality, liveable areas. On the other hand, greater motorisation and the availability to perform leisure or work activities while travelling in AVs could increase the number of trips and travel distances, encouraging urban traffic congestion and sprawl. These diverse, and sometimes conflicting, estimates and opinions give rise to considerable uncertainty among urban policy decision-makers, sometimes leading to planning inaction. This paper aims to shed light on the opportunities that AVs offer in delivering attractive, healthy and sustainable urbanisation patterns. This paper employs a backcasting approach to investigate whether and how the potential impacts of AV implementation can support or threaten a range of urban development policy goals. This approach enables conflicts between policy goals to be identified. The findings point to the need for mixed-use development policy, the clustering of urban facilities and services, the restriction of motorized access in cities and the adoption of shared high-quality multimodal transport.     

Relay Requirement and Traffic Assignment of Electric Vehicles

This article defines, formulates, and solves a new equilibrium traffic assignment problem with side constraints—the traffic assignment problem with relays. The relay requirement arises from the driving situation that the onboard fuel capacity of vehicles is lower than what is needed for accomplishing their trips and the number and distribution of refueling infrastructures over the network are under the expected level. We proposed this problem as a modeling platform for evaluating congested regional transportation networks that serve plug‐in electric vehicles (in addition to internal combustion engine vehicles), where battery‐recharging or battery‐swapping stations are scarce. Specifically, we presented a novel nonlinear integer programming formulation, analyzed its mathematical properties and paradoxical phenomena, and suggested a generalized Benders decomposition framework for its solutions. In the algorithmic framework, a gradient projection algorithm and a labeling algorithm are adopted for, respectively, solving the primal problem and the relaxed master problem—the shortest path problem with relays. The modeling and solution methods are implemented for solving a set of example network problems. The numerical analysis results obtained from the implementation clearly show how the driving range limit and relay station location reshape equilibrium network flows.     

A Trip Distribution Model Involving Spatial and Dominance Attributes

Abstract: Modeling the destination choice is a difficult task and very often it represents the weakest step in travel demand modeling. This weakness is mainly due to the high number of potential alternatives and to the very limited number of available attributes. Indeed, the alternative destinations in a distribution model are generally all the (hundreds in a medium large size city) traffic zones identified in the zoning phase. Moreover, the most widely adopted specification of random utility (RU) destination choice models introduces just two categories of attributes: attractiveness attributes of the destination zone and “impedance” attributes reproducing the origin‐destination generalized cost. Through this kind of attribute alone it is quite difficult reproducing the real choice context faced by the decision maker, who generally knows only a limited part of the study area with sufficient detail to evaluate its attractiveness and the generalized transport costs of reaching it. In this regard, our article proposes two sets of new dummy‐like attributes to be used within the destination choice models to identify, within the whole choice set, a smaller subset of zones (those with nonzero value of dummy‐like attributes) more/less likely to be perceived. The former are generated by extending and applying the concept of dominance among alternatives to the framework of RU theory and can be used to identify a set of alternatives less likely to be perceived (exclusion variables) whose systematic utility will be penalized as a function of these variables. The latter are spatial variables reproducing better knowledge of zones with a privileged spatial position and can be used to identify a set of alternatives more likely to be perceived (selection variables) whose systematic utility will be improved as a function of these variables. These new attributes are tested on empirical data related to nonsystematic trips in Rome, Italy. It is also important to underline that the proposed dominance variables can be conveniently used in any other choice context.     

A model of multi-purpose shopping trip behavior

Existing utility-based models of complex choice behavior do not adequately deal with the interdependencies of chained choices. In this paper, we introduce a model of multi-purpose shopping which is aimed at overcoming this shortcoming. In the proposed model, dependencies between choices within as well as between trips are covered by a recursive definition of trip utility. The standard log-likelihood estimation procedure is used to calibrate the model. Simulation experiments show that estimation results are satisfactorily accurate and robust. Comparison of the model to a conventional choice model using simulated data indicates that even low tendencies to make multi-purpose trips have a significant influence on predicted destination choice. Furthermore, it is shown that conventional models do not satisfactorily predict simulated multi-purpose behavior.     

Solving the first‐mile ridesharing problem using autonomous vehicles

Autonomous vehicles (AVs) are paving a way to reshape the operation and management of urban transportation systems by improving travel mobility. In this study, we propose to use AVs for solving the first‐mile (FM) problem that aims to transport passengers from their homes to metro stations. Passengers submit travel requests in advance and a fleet operator dispatches AVs and arranges ridesharing in a rolling horizon framework. The ridesharing is implemented by assigning one AV to serve several requests subject to the vehicle capacity, the maximum travel time, and the accessibility constraints. A mixed integer linear programming (MILP) model is formulated to determine the AV dispatch and ridesharing schemes for the minimum operational costs. Then, another MILP model with the objective of the maximum user satisfaction is formulated for the purpose of comparison. A cluster‐based solution method is designed to deal with the large‐scale FM problem. Extensive numerical experiments are conducted to demonstrate the application of the proposed method. Results show that ridesharing can reduce both required AV fleet size and vehicle traveled miles. The proposed solution method is able to solve the problem efficiently and fulfil the requirement of online computation.     

Allocation of Link Flow Detectors for Origin‐Destination Matrix Estimation—A Comparative Study

Abstract: Origin‐destination (OD) matrices are essential for various analyses in the field of traffic planning, and they are often estimated from link flow observations. We compare methods for allocating link flow detectors to a traffic network with respect to the quality of the estimated OD‐matrix. First, an overview of allocation methods proposed in the literature is presented. Second, we construct a controlled experimental environment where any allocation method can be evaluated, and compared to others, in terms of the quality of the estimated OD‐matrix. Third, this environment is used to evaluate and compare three fundamental allocation methods. Studies are made on the Sioux Falls network and on a network modeling the city of Linköping. Our conclusion is, that the most commonly studied approach for detector allocation, maximizing the coverage of OD‐pairs, seems to be unfavorable for the quality of the estimated OD‐matrix.     

An optimization‐based traffic diversion model during construction closures

During major highway construction, when lanes or entire highway sections must be temporarily closed, traffic managers would like to inform motorists of alternative routes around the construction site well in advance of the project location. This study develops a traffic diversion model to propose an optimum alternate route to drivers during a construction activity. The models and algorithms developed in this study assess a potential diversion route to optimize network performance while considering the drivers’ behaviors in following the proposed alternate route during a closure. A bilevel optimization model is proposed to minimize the total travel time of the affected network considering the link closure and a proposed alternate route for the travelers. A travelers’ route choice decision is modeled based on the user equilibrium traffic assignment, whereas a certain percentage of drivers are assumed to divert to the recommended alternate route. A sufficiently large subnetwork is selected, and a path selection method is proposed to reduce the computational effort required to optimize the model. A set of simulation experiments is conducted using the Tarrant County network in north Texas. The results show the ability of the model to improve the overall network performance during hypothetical closure scenarios.     

Smart dynamic evacuation planning and online management using vehicular communication system

During disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for destination choice and a dynamic traffic assignment (DTA) for path choice toward the chosen destinations to obtain an initial evacuation plan. The initial plan is then enhanced by employing a vehicular ad hoc network (VANET) within the vehicular edge computing (VEC) architecture. This enhancement enables communication among evacuees, allowing them to revise their vehicle's route choice and planned destination. These revisions take into account the changing risk and traffic conditions. The proposed online DPE framework is applied to the real evacuation scenario of Mill Valley City, CA. The proposed model is evaluated with different VANET architectures, including vehicular cloud computing (VCC) and VEC. The results show that the VEC framework outperforms other configurations and improves the evacuation process compared to the scenario with an initial plan by more than 30% in network clearance time. Additionally, a performance analysis is carried out for evacuation scenarios with different penetration rates of connected vehicles in VANET.     

A Network Generalized Extreme Value Model for Route Choice Allowing Implicit Route Enumeration

This article introduces an adaptation of the Network GEV model for modelling joint choices, named Joint Network GEV (JNG), and its application to the route choice context, named Link‐Based JNG (LB‐JNG), assuming the choice of a route as the joint choice of all links belonging to that route. The LB‐JNG model aims at reproducing the effects of routes overlapping with a theoretical robust framework (since it belongs to the Network GEV, to date the most flexible closed‐form model in reproducing covariances), allowing at the same time for easy application to real networks through a closed‐form probability statement, a proper definition of its parameters and the availability of an implicit route enumeration algorithm for network loading. The article carries out first an overview of the theoretical properties of the JNG model. Then, the LB‐JNG adaptation to route choice is described, and its capability to reproduce the effects of routes overlapping is investigated using some test networks, wherein the performances of the proposed model are also compared with those of other route choice models available in the literature. Finally, an implicit route enumeration algorithm for macroscopic static stochastic network loading, based on a double‐step generalization of Dial's STOCH algorithm, is proposed and tested on real size networks.     

Integrated Planning of Supply Chain Networks and Multimodal Transportation Infrastructure Expansion: Model Development and Application to the Biofuel Industry

Abstract: As the biofuel industry continues to expand, the construction of new biorefinery facilities induces a huge amount of biomass feedstock shipment from supply points to the refineries and biofuel shipment to the consumption locations, which increases traffic demand in the transportation network and contributes to additional congestion (especially in the neighborhood of the refineries). Hence, it is beneficial to form public‐private partnerships to simultaneously consider transportation network expansion and biofuel supply chain design to mitigate congestion. This article presents an integrated mathematical model for biofuel supply chain design where the near‐optimum number and location of biorefinery facilities, the near‐optimal routing of biomass and biofuel shipments, and possible highway/railroad capacity expansion are determined. The objective is to minimize the total cost for biorefinery construction, transportation infrastructure expansion, and transportation delay (for both biomass/biofuel shipment and public travel) under congestion. A genetic algorithm framework (with embedded Lagrangian relaxation and traffic assignment algorithms) is developed to solve the optimization model, and an empirical case study for the state of Illinois is conducted with realistic biofuel production data. The computational results show that the proposed solution approach is able to solve the problem efficiently. Various managerial insights are also drawn. It shall be noted that although this article focuses on the booming biofuel industry, the model and solution techniques are suitable for a number of application contexts that simultaneously involve network traffic equilibrium, infrastructure expansion, and facility location choices (which determine the origin/destination of multi‐commodity flow).     

Modeling evacuation route choices under influence of variable message signs

This work develops a novel methodology for investigating dynamic evacuation route switching behavior and its influence on evacuation traffic assignment considering evacuees’ intuitionistic fuzzy perception and cognition of multitype multiattribute real‐time variable message sign (VMS) information. The methodology has three distinctive features. First, a link‐based VMS information utility model is developed. Second, two link‐specific dynamic escape speed models (DESMs), characterized by VMS information, evacuee attributes, and hazard‐related information, are proposed. Third, VMS information characteristics and the intuitionistic fuzzy method are integrated into route choice behavior models. The link‐specific DESMs are used to adjust the reactive DYNASTOCH algorithm to simulate emergency traffic evacuation. Results demonstrate that the proposed models are effective in characterizing not only dynamic evacuation route switching behavior (microscopic) but also network‐wide evacuation traffic performances (macro) under behavioral and information uncertainties.     

Graph‐Based Modeling of Information Flow Evolution and Propagation under V2V Communications‐Based Advanced Traveler Information Systems

A vehicle equipped with a vehicle‐to‐vehicle (V2V) communications capability can continuously update its knowledge on traffic conditions using its own experience and anonymously obtained travel experience data from other such equipped vehicles without any central coordination. In such a V2V communications‐based advanced traveler information system (ATIS), the dynamics of traffic flow and intervehicle communication lead to the time‐dependent vehicle knowledge on the traffic network conditions. In this context, this study proposes a graph‐based multilayer network framework to model the V2V‐based ATIS as a complex system which is composed of three coupled network layers: a physical traffic flow network, and virtual intervehicle communication and information flow networks. To determine the occurrence of V2V communication, the intervehicle communication layer is first constructed using the time‐dependent locations of vehicles in the traffic flow layer and intervehicle communication‐related constraints. Then an information flow network is constructed based on events in the traffic and intervehicle communication networks. The graph structure of this information flow network enables the efficient tracking of the time‐dependent vehicle knowledge of the traffic network conditions using a simple graph‐based reverse search algorithm and the storage of the information flow network as a single graph database. Further, the proposed framework provides a retrospective modeling capability to articulate explicitly how information flow evolves and propagates. These capabilities are critical to develop strategies for the rapid flow of useful information and traffic routing to enhance network performance. It also serves as a basic building block for the design of V2V‐based route guidance strategies to manage traffic conditions in congested networks. Synthetic experiments are used to compare the graph‐based approach to a simulation‐based approach, and illustrate both memory usage and computational time efficiencies.     

Short‐Term Traffic Speed Prediction for an Urban Corridor

Short‐term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real‐time prediction of traffic speeds can support travellers’ route choices and traffic guidance/control. In this article, a support vector machine model (single‐step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short‐term traffic speed prediction model is developed based on the single‐step prediction model. To test the accuracy of the proposed short‐term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short‐term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial‐temporal parameters exhibits good performance compared with an artificial neural network, a k‐nearest neighbor model, a historical data‐based model, and a moving average data‐based model.     

Patrol Route Planning for Incident Response Vehicles under Dispatching Station Scenarios

Traffic incidents often contribute to major safety concerns, impose additional congestion in the neighboring transportation networks, and induce indirect costs to economy. As roughly a third of traffic crashes are secondary accidents, effective incident management activities are critical, especially on roadways with high traffic volume, to detect, respond to, and clean up incidents in a timely fashion, which supports safety constraints and restores traffic capacity in the transportation network. Hence, it is beneficial to simultaneously plan for first respondents’ dispatching station location and patrol route design to mitigate congestion. This article presents an optimal route planning for patrolling vehicles to facilitate quick response to potential accidents. A mixed‐integer nonlinear program is proposed that minimizes the respondents’ patrolling travel cost based on the expected maximum response time from each arbitrary location to all incident locations (a.k.a. hotspots) with various incident occurrence probabilities. We have developed a column generation‐based solution technique to solve the route optimization model under different station design scenarios. To investigate the impact of dispatching station design on the routing cost, an integrated genetic algorithm framework with embedded continuous approximation approach is developed that reduces the complexity of the hybrid location design and route planning problem. Numerical experiments on hypothetical networks of various sizes are conducted to indicate the performance of the proposed algorithm and to draw managerial insights. The models and solution techniques, developed in this article, are applicable to a number of network problems that simultaneously involve routing and facility location choices.     

An iterative learning approach for network contraction: Path finding problem in stochastic time‐varying networks

Path finding problem has a broad application in different fields of engineering. Travel time uncertainty is a critical factor affecting this problem and the route choice of transportation users. The major downside of the existing algorithms for the reliable path finding problem is their inefficiency in computational time. This study aims to develop a network contraction approach to reduce the network size of each specific origin and destination (OD) pair in stochastic time‐dependent networks. The network contraction is based on the comparison of optimistic and pessimistic solutions resulting from minimum and maximum travel time realizations of a Monte‐Carlo simulation (MCS)‐based approach. In this respect, the researchers propose a learning approach to utilize the information of the realizations in the initial iterations of the MCS approach. Implementation of this approach is in place for several OD pairs of two real‐world large‐scale applications. First, it is calibrated for the Chicago downtown network; the performance and accuracy of the proposed approach are investigated by comparing the results against that of the approach without any network contraction. In addition, the Salt Lake City network illustrates the transferability of the approach to other networks. The results demonstrate significant computational improvements, with an acceptable accuracy level relative to the approach without network contraction.     

Using car parking requirements to promote sustainable transport development in the Kingdom of Saudi Arabia

The aims of this paper are to review the current practices of car parking standards in the UK, the USA, and the Kingdom of Saudi Arabia (KSA); to draw the main lessons learned from experiences of the UK and the USA; and to suggest suitable improvements in the standards of car parking requirements in the KSA. The paper follows a comparative approach in reviewing car parking standard requirements according to different types of land use in the UK, the USA, and the KSA. The article illustrates that transportation planners in the UK and the USA use parking policies, especially the application of car parking standards, along with other planning and transport measures to promote sustainable transport choices, to reduce reliance on cars, and to reduce traffic and air pollution. Local authorities in the UK and the USA have moved from requiring minimum standards for car parking to maximum standards. The aim of maximum parking standards is to decrease the number of trips made by private cars whilst seeking to maintain and enhance the viability of economic centres and support sustainable development. In the KSA, authorities still adhere to minimum car parking standards. Moreover, the car parking requirements are not correlated with city zoning systems. The paper concludes with some thoughts on how to improve the current parking requirements in KSA cities.     

大城市中心区停车设施供应与路网容量关系

介绍了停车设施供应与路网容量的关系。在传统的停车需求预测方法的基础上,具体分析了停车设施供应与需求的关系、中心区交通状况、高峰时段的停车需求、路网容量,从而建立停车设施供应与路网容量平衡关系模型。提出了建立两者合理平衡的对策和方法,可为大城市中心区静态交通规划提供借鉴。     

Demand Profiling for Dynamic Traffic Assignment by Integrating Departure Time Choice and Trip Distribution

One challenge in dynamic traffic assignment (DTA) modeling is estimating the finely disaggregated trip matrix required by such models. In previous work, an exogenous time distribution profile for trip departure rates is applied uniformly across all origin‐destination (O‐D) pairs. This article develops an endogenous departure time choice model based on an arrival time penalty function incorporated into trip distribution, which results in distinct demand profiles by O‐D pair. This yields a simultaneous departure time and trip choice making use of the time‐varying travel times in DTA. The required input is arrival time preferences, which can be disaggregated by O‐D pair and may be easier to collect through surveys than the demand profile. This model is integrated into the four‐step planning process with feedback, creating an extension of previous frameworks which aggregate over time. Empirical results from a network representing Austin, Texas indicate variation in departure time choice appropriate to the arrival time penalties and travel times. Our model also appears to converge faster to a dynamic trip table prediction than a time‐aggregated coupling of DTA and planning, potentially reducing the substantial computation time of combined planning models that solve DTA as a subproblem of a feedback process.     

A Dynamic Route Choice Model Considering Uncertain Capacities

Abstract: The standard assumption in (dynamic) traffic assignment models is that route choice is solely determined by a (perceived) deterministic travel time. However, recently, there is a growing interest in (dynamic) equilibrium route choice models in which travelers not only select their paths based on an estimated deterministic travel time, but also based on travel time reliability, in this article defined as the probability that the actual travel time deviates from the anticipated value. We extend the linear programming cell transmission model‐based dynamic traffic assignment (LP CTM‐DTA) model to account for travelers’ consideration of uncertainty regarding saturation flow rates (in this article referred to as capacities). It is shown that these reliability considerations can be accounted for by simply reducing the road capacities appearing in the constraint set of the classical LP CTM‐DTA model. More importantly, we provide results on the amount of capacity reduction necessary to ensure a certain reliability level. Although in the proposed model any probability distribution can be used to model the uncertainty, the selection of a specific probability distribution can potentially be burdensome for the modeler. To this end, we also present results on the class of symmetric probability distributions that has been particularly popular in the robust optimization literature. Properties for this broad class of distributions will be derived within the context of the introduced model. In numerical case studies, the model predicts that travel patterns can be significantly different when accounting for travelers’ reliability considerations.     

东海企业总部经济区整体交通组织分析

介绍了东海企业总部经济区在地块高强度开发的情况下,从满足企业总部经济区建设需要的交通出发,基于区域总体布局,利用其他相关总部经济区的成功经验,提出合理的交通组织规划。着重介绍了5项建议:建立轨交与普通公交为主的公交体系;加大路网密度;局部形成步行交通网络;严格控制停车位总量;形成地下停车系统。交通组织分析将为企业总部经济区项目建设提供设计条件的依据。