Transportation network design for maximizing space–time accessibility

Lu, Tong; Zhou, Xuesong; Miller, Harvey J.

doi:10.1016/j.trb.2015.08.002

Cited by 143 publications

(47 citation statements)

References 65 publications

(71 reference statements)

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“…The GAMS commercial software with CPLEX and IPOPT solvers were employed to solve the transformed models. By constructing a time-dependent spacetime network, Tong et al (2015) formulated a linear integer programming model for urban NDP to maximize the number of accessible activity locations within travel time budget, and Lagrangian relaxation algorithm was designed to solve the proposed model. For some more researches, interested readers can refer to Magnanti and Wong (1984), Li et al (2012), Farahani et al (2013), etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As pointed by , for multiple trains, the scheduling problem can be transformed into a coordinate routing problem in their space-time networks. For some other related spacetime network modeling methods and its extension, interesting readers can refer to Xing and Zhou (2011), , Tong et al (2015), Mahmoudi and Zhou (2016) and Ruan et al (2016).…”

Section: Space-time Networkmentioning

confidence: 99%

See 1 more Smart Citation

Integrated multi-track station layout design and train scheduling models on railway corridors

Yang

Gao

et al. 2016

Transportation Research Part C: Emerging Technologies

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Section: Literature Reviewmentioning

confidence: 99%

Section: Space-time Networkmentioning

confidence: 99%

Integrated multi-track station layout design and train scheduling models on railway corridors

Yang

Gao

et al. 2016

Transportation Research Part C: Emerging Technologies

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“…For other kinds of constraints that cannot be converted into the three types examined here, the dynamic generation properties of solving the GA-based network path may be helpful in solving such problems according to the generation-refinement paradigm (e.g., multiconstraint transportation network design [42,43] and transit network analysis [12,44]). With the network represented in the GA framework, the route in the network can be directly generated and refined according to the properties of the algebra system.…”

Section: Potential Application Of This Template-based Approachmentioning

confidence: 99%

Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

Luo

et al. 2018

IJGI

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The process of searching for a dynamic constrained optimal path has received increasing attention in traffic planning, evacuation, and personalized or collaborative traffic service. As most existing multiple constrained optimal path (MCOP) methods cannot search for a path given various types of constraints that dynamically change during the search, few approaches for dynamic multiple constrained optimal path (DMCOP) with type II dynamics are available for practical use. In this study, we develop a method to solve the DMCOP problem with type II dynamics based on the unification of various types of constraints under a geometric algebra (GA) framework. In our method, the network topology and three different types of constraints are represented by using algebraic base coding. With a parameterized optimization of the MCOP algorithm based on a greedy search strategy under the generation-refinement paradigm, this algorithm is found to accurately support the discovery of optimal paths as the constraints of numerical values, nodes, and route structure types are dynamically added to the network. The algorithm was tested with simulated cases of optimal tourism route searches in China's road networks with various combinations of constraints. The case study indicates that our algorithm can not only solve the DMCOP with different types of constraints but also use constraints to speed up the route filtering.Two different types of dynamics can increase the complexity of the DMCOP problem. With type I dynamics, the network condition, such as the weight or topology, changes during optimal path searching; the constraints are not altered. For example, in adaptive dynamic traffic navigation, the optimal path should be dynamically generated with the traffic conditions, where the weights are dynamically changed according to the current traffic status. With type II dynamics, the network conditions are not changed; however, the constraints used to restrict the searching of the optimal path change with time. One example is event-forced constraints, which are not determined unless a certain kind of event (e.g., emergency or user interactions) occurs. In this way, the constraints should be changed according to the specific schedule of the event dynamically occurring. Since these different constraints cannot be known in advance, these constraints should be added dynamically during route planning [15]. For instance, in a typical scenario of a long-distance self-driving tour, the navigation may first start from route planning with a start point and an end point. During the travel, users may add location constraints [16], reliability constraints [17], turn constraints [18] for intercity traffic and cycle routes [19], or satisfaction constraints [20]. Certain emergency cases may also exist that require the planned route to be dynamically adjusted in a short amount of time according to a series of constraints.Previous initial studies have addressed the solution of DMCOP with type I dynamics (i.e., network status changes) [21]. Schott and Staples ...

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“…The space-time network was first proposed by Cooke and Halsey (1966) in finding the time-dependent shortest path by including waiting links in physical networks in order to get vehicle time-dependent shortest path trajectories. The concept of space-time networks has been adapted in many applications of the network design problem, for example, transportation road network design for maximizing space-time accessibility (Tong et al, 2015). In a space-time network, travelers can be modeled as individual agents with their own departure time and the least-cost path from given origins to destinations.…”

Section: Space-time Network Representation For Rg Problemmentioning

confidence: 99%

Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

Mirchandani

Zhou

2015

Transportation Research Part B: Methodological

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a b s t r a c tThis paper addresses the problem of simultaneous route guidance and traffic signal optimization problem (RGTSO) where each vehicle in a traffic network is guided on a path and the traffic signals servicing these vehicles are set to minimize their travel times. The network is modeled as a space-phase-time (SPT) hyper-network to explicitly represent the traffic signal control phases and time-dependent vehicle paths. A Lagrangianrelaxation-based optimization framework is proposed to decouple the RGTSO problem into two subproblems: the Route Guidance (RG) problem for multiple vehicles with given origins and destinations and the Traffic Signal Optimization (TSO) problem. In the RG subproblem, the route of each vehicle is provided subject to time-dependent link capacities imposed by the solution of the TSO problem, while the traffic signal timings are optimized according to the respective link travel demands aggregated from the vehicle trajectories. The dual prices of the RG subproblem indicate search directions for optimization of the traffic signal phase sequences and durations in the TSO subproblem. Both RG and TSO subproblems can be solved using a computationally efficient finite-horizon dynamic programming framework, enhanced by parallel computing techniques. Two numerical experiments demonstrated that the system optimum of the RGTSO problem can be quickly reached with relatively small duality gap for medium-size urban networks.

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Transportation network design for maximizing space–time accessibility

Cited by 143 publications

References 65 publications

Integrated multi-track station layout design and train scheduling models on railway corridors

Integrated multi-track station layout design and train scheduling models on railway corridors

Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

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