Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

Toll optimization in a large‐scale dynamic traffic network is typically characterized by an expensive‐to‐evaluate objective function. In this paper, we propose two toll‐level problems (TLPs) integrated with a large‐scale simulation‐based dynamic traffic assignment model of Melbourne, Australia. The first TLP aims to control the pricing zone (PZ) through a time‐varying joint distance and delay toll such that the network fundamental diagram (NFD) of the PZ does not enter the congested regime. The second TLP is built upon the first TLP by further considering the minimization of the heterogeneity of congestion distribution in the PZ. To solve the two TLPs, a computationally efficient surrogate‐based optimization method, that is, regressing kriging with expected improvement sampling, is applied to approximate the simulation input–output mapping, which can balance well between local exploitation and global exploration. Results show that the two optimal TLP solutions reduce the average travel time in the PZ (entire network) by 29.5% (1.4%) and 21.6% (2.5%), respectively. Reducing the heterogeneity of congestion distribution achieves higher network flows in the PZ and a lower average travel time or a larger total travel time saving in the entire network.     

Time‐Dependent Discrete Network Design Frameworks Considering Land Use

Abstract: This article proposes optimization frameworks for discrete road network design considering the land‐use transport interaction over time. Unlike existing models, the optimization frameworks can determine the optimal designs automatically without trial‐and‐error once the objective(s) is/are clearly defined. Moreover, these frameworks allow the evaluation of the impacts of the optimal designs on the related parties including landowners, toll road operators, transit operators, and road users, and help network planners and profit‐makers with decision making by eliminating many alternative designs. A numerical study is set up to examine road network design's effects on these related parties under three road construction schemes: exact cost recovery, build‐operate‐transfer, and cross‐subsidization. The results show that the changes in landowner profits are not the same after implementing any scheme. These unequal changes raise the issue of the landowner equity. This implies that the government has to consider trade‐offs between parties’ objectives carefully.     

Dynamic Shared‐Taxi Dispatch Algorithm with Hybrid‐Simulated Annealing

Taxi is certainly the most popular type of on‐demand transportation service in urban areas because taxi‐dispatching systems offer more and better services in terms of shorter wait times and passenger travel convenience. However, a shortage of taxicabs has always been critical in many urban contexts especially during peak hours, and taxi has great potential to maximize its efficiency by employing the shared‐ride concept. There are recent successes in dynamic ride‐sharing projects that are expected to bring substantial benefits arising from energy consumption and operation efficiency and thus, it is essential to develop advanced shared‐taxi‐dispatch algorithms and investigate the collective benefits of dynamic ride‐sharing by maximizing occupancy and minimizing travel times in real‐time. This article investigates how taxi services can be improved by proposing shared‐taxi algorithms and what type of objective functions and constraints could be employed to prevent excessive passenger detours. Hybrid‐simulated annealing (HSA) is applied to dynamically assign passenger requests efficiently. A series of simulations are conducted with two different taxi operation strategies. The simulation results reveal that allowing ride‐sharing for taxicabs increases productivity over the various demand levels and HSA can be considered as a suitable solution to maximize the system efficiency of dynamic ride‐sharing.     

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

This article presents a novel real‐time traffic network management system using an end‐to‐end deep learning (E2EDL) methodology. A computational learning model is trained, which allows the system to identify the time‐varying traffic congestion pattern in the network, and recommend integrated traffic management schemes to reduce this congestion. The proposed model structure captures the temporal and spatial congestion pattern correlations exhibited in the network, and associates these patterns with efficient traffic management schemes. The E2EDL traffic management system is trained using a laboratory‐generated data set consisting of pairings of prevailing traffic network conditions and efficient traffic management schemes designed to cope with these conditions. The system is applied for the US‐75 corridor in Dallas, Texas. Several experiments are conducted to examine the system performance under different traffic operational conditions. The results show that the E2EDL system achieves travel time savings comparable to those recorded for an optimization‐based traffic management system.     

A Dantzig‐Wolfe Decomposition‐Based Heuristic for Off‐line Capacity Calibration of Dynamic Traffic Assignment

Abstract: One of the critical elements in considering any real‐time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. Dynamic traffic assignment (DTA) is therefore gaining wider acceptance among agencies and practitioners as a more realistic representation of traffic phenomena than static traffic assignment. Though it is imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies, DTA calibration is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. To compliment other research on behavioral and trip table issues, this work focuses on DTA capacity calibration and presents an efficient Dantzig‐Wolfe decomposition‐based heuristic that decomposes the problem into a restricted master problem and a series of pricing problems. The restricted master problem is a capacity manipulation problem, which can be solved by a linear programming solver. The pricing problem is the user optimal DTA which can be optimally solved by an existing combinatorial algorithm. In addition, the proposed set of dual variable approximation techniques is one of a very limited number of approaches that can be used to estimate network‐wide dual information in facilitating algorithmic designs while maintaining scalability. Two networks of various sizes are empirically tested to demonstrate the efficiency and efficacy of the proposed heuristic. Based on the results, the proposed heuristic can calibrate the network capacity and match the counts within a 1% optimality gap.     

Strategic network expansion of urban rapid transit systems: A bi‐objective programming model

With the development of urbanization and the extension of city boundaries, the expansion of rapid transit systems based on the existing lines becomes an essential issue in urban transportation systems. In this study, the network expansion problem is formulated as a bi‐objective programming model to minimize the construction cost and maximize the total travel demand covered by the newly introduced transit lines. To solve the bi‐objective mixed‐integer linear program, an approach called minimum distance to the utopia point is applied. Thus, the specific trade‐off is suggested to the decision makers instead of a series of optimal solutions. A real‐world case study based on the metro network in Wuxi, China, is conducted, and the results demonstrate the effectiveness and efficiency of the proposed model and solution method. It is found that the utopia method can not only provide a reasonable connecting pattern of the network expansion problem but also identify the corridors with high priority under the limited budget condition.     

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.     

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

This article develops an efficient methodology to optimize the timing of signalized intersections in urban street networks. Our approach distributes a network‐level mixed‐integer linear program (MILP) to intersection level. This distribution significantly reduces the complexity of the MILP and makes it real‐time and scalable. We create coordination between MILPs to reduce the probability of finding locally optimal solutions. The formulation accounts for oversaturated conditions by using an appropriate objective function and explicit constraints on queue length. We develop a rolling‐horizon solution algorithm and apply it to several case‐study networks under various demand patterns. The objective function of the optimization program is to maximize intersection throughput. The comparison of the obtained solutions to an optimal solution found by a central optimization approach (whenever possible) shows a maximum of 1% gap on a number of performance measures over different conditions.     

Sensitivity Analysis of Logit-Based Stochastic User Equilibrium Network Flows with Entry–Exit Toll Schemes

Abstract:   This article addresses sensitivity analysis of logit-based stochastic user equilibrium (SUE) network flows on transportation networks with entry–exit toll schemes. These schemes have a technical challenge that the generalized travel time including tolls on a path is nonadditive. First, it shows that the perturbed logit-based network flows—link and entry–exit flows—are implicit but continuously differentiable functions with respect to the perturbation parameters arising from link travel time functions, origin–destination demands, and entry–exit tolls. Second, this article derives elegant analytical expressions of gradients of perturbed logit-based SUE network flows, and elaborates that these gradients can be calculated by the two-stage stochastic network loading algorithm, which obviates the need for path enumeration. Finally, two numerical examples are presented to demonstrate the calculation process and computational capability .     

Cyber‐physical approach to the optimization of semiactive structural control under multiple earthquake ground motions

This paper presents a cyber‐physical approach to optimize the semiactive control of a base‐isolated structure under a suite of earthquakes. The approach uses numerical search algorithms to guide the exploration of the design space and real‐time hybrid simulation (RTHS) to evaluate candidate designs, creating a framework for real‐time hybrid optimization (RHTO). By supplanting traditional numerical analysis (i.e., finite element methods) with RTHS, structural components that are difficult to model can be represented accurately while still capturing global structural performance. The efficiency of RTHO is improved for multiple design excitations with the creation of a multiinterval particle swarm optimization (MI‐PSO) algorithm. As a proof‐of‐concept, RTHO is applied to improve the seismic performance of a base‐isolated structure with supplemental control. The proposed RTHO framework with MI‐PSO is a versatile technique for multivariate optimization under multiple excitations. It is well suited for the accurate and rapid evaluation of structures with nonlinear experimental substructures, in particular, those that do not undergo permanent damage such as structural control devices. The RTHO framework integrates popular optimization algorithms with advanced experimental methods, creating an exciting new cyber‐physical approach to design.     

区域运输通道网络设计研究

运用离散优化方法,研究区域内各组团间运输通道网络优化设计问题,设定运输通道供给能力以及通道在区域之间的运输时间约束,构造综合运输通道投资建设总费用的优化模型,考虑各种运输通道的单位投资建设费用和建设长度的最优问题;利用综合运输通道的特点和区域组团网络结构进行算法设计,并给出最优解的多方案求解步骤;针对大规模节点采用遗传算法进行求解可以提高求解效率;通过一个算例,对模型和算法的可行性和有效性进行验证,说明该算法可得到综合运输通道的最优投资方案,使综合运输通道满足规划年的客货运需求,以及各种运输方式的平均出行时间满足要求,分析说明客货运输需求与运输通道建设的关系,结果表明提出的模型与算法对交通投资项目提供较好的辅助决策支持。     

Zoning‐Based Vertical Transportation Optimization for Workers at Peak Time in a Skyscraper Construction

In a skyscraper construction, a great number of workers and materials must be vertically transported to the proper positions depending on their roles. Particularly, the optimal vertical transportation logistics of lift cars for workers at peak time should be established to enhance the entire project performance in a skyscraper construction. For achieving this objective, the zoning‐based concept can be introduced to improve the effectiveness of the vertical transportation logistics of lift cars for workers at peak time in a skyscraper construction. In developing the zoning‐based vertical transportation logistics, it is necessary to consider the minimization of the electricity consumption as an environmental index as well as the minimization of the operating time and the maximization of the cost effectiveness. Therefore, this study aims to develop a multi‐objective optimization model for solving the time–cost–environment trade‐off problem in establishing the zoning‐based vertical transportation logistics of lift cars for workers at peak time in a skyscraper construction.     

Development of Recurrent Neural Network Considering Temporal‐Spatial Input Dynamics for Freeway Travel Time Modeling

Abstract: The artificial neural network (ANN) is one advance approach to freeway travel time prediction. Various studies using different inputs have come to no consensus on the effects of input selections. In addition, very little discussion has been made on the temporal–spatial aspect of the ANN travel time prediction process. In this study, we employ an ANN ensemble technique to analyze the effects of various input settings on the ANN prediction performances. Volume, occupancy, and speed are used as inputs to predict travel times. The predictions are then compared against the travel times collected from the toll collection system in Houston. The results show speed or occupancy measured at the segment of interest may be used as sole input to produce acceptable predictions, but all three variables together tend to yield the best prediction results. The inclusion of inputs from both upstream and downstream segments is statistically better than using only the inputs from current segment. It also appears that the magnitude of prevailing segment travel time can be used as a guideline to set up temporal input delays for better prediction accuracies. The evaluation of spatiotemporal input interactions reveals that past information on downstream and current segments is useful in improving prediction accuracy whereas past inputs from the upstream location do not provide as much constructive information. Finally, a variant of the state‐space model (SSNN), namely time‐delayed state‐space neural network (TDSSNN), is proposed and compared against other popular ANN models. The comparison shows that the TDSSNN outperforms other networks and remains very comparable with the SSNN. Future research is needed to analyze TDSSNN's ability in corridor prediction settings.     

Column Generation‐Based Approach for Solving Large‐Scale Ready Mixed Concrete Delivery Dispatching Problems

Ready mix concrete (RMC) dispatching forms a critical component of the construction supply chain. However, optimization approaches within the RMC dispatching continue to evolve due to the specific size, constraints, and objectives required of the application domain. In this article, we develop a column generation algorithm for vehicle routing problems (VRPs) with time window constraints as applied to RMC dispatching problems and examine the performance of the approach for this specific application domain. The objective of the problem is to find the minimum cost routes for a fleet of capacitated vehicles serving concrete to customers with known demand from depots within the allowable time window. The VRP is specified to cover the concrete delivery problem by adding additional constraints that reflect real situations. The introduced model is amenable to the Dantzig–Wolfe reformulation for solving pricing problems using a two‐staged methodology as proposed in this article. Further, under the mild assumption of homogeneity of the vehicles, the pricing sub‐problem can be viewed as a minimum‐cost multi‐commodity flow problem and solved in polynomial time using efficient network simplex method implementations. A large‐scale field collect data set is used for evaluating the model and the proposed solution method, with and without time window constraints. In addition, the method is compared with the exact solution found via enumeration. The results show that on average the proposed methodology attains near optimal solutions for many of the large sized models but is 10 times faster than branch‐and‐cut.     

A Clustering Algorithm for Bi‐Criteria Stop Location Design with Elastic Demand

This article proposes a bi‐criteria formulation to find the optimal location of light rapid transit stations in a network where demand is elastic and budget is constrained. Our model is composed of two competing objective functions seeking to maximize the total ridership and minimize the total budget allocated. In this research, demand is formulated using the random utility maximization method with variables including access time and travel time. The transit station location problem of this study is formulated using mixed integer programming and we propose a heuristic solution algorithm to solve large‐scale instances which is inspired by the problem context. The elastic demand is integrated with the optimization problem in an innovative way which facilitates the solution process. The performance of our model is evaluated on two test problems and we carry out its implementation on a real‐world instance. Due to the special shape of the Pareto front function, significant practical policy implications, in particular budget allocation, are discussed to emphasize the fact that the trade‐off between cost and benefit may result in large investments with little outcomes and vice versa.     

Optimal design of real‐size building structures using quantum‐behaved developed swarm optimizer

In this paper, the quantum‐behaved developed swarm optimizer is proposed for optimal design of real‐size building structures in which the quantum computing is introduced into the standard developed swarm optimizer. In this method, the position‐updating process for the search agents is conducted by simultaneous utilization of the so far best position of all particles, center of mass of all particles, so far best position of each particle, and the mean best position of all particles in which the first two of these aspects satisfy the exploration phase of the algorithm, whereas the other two are utilized for improving the exploitation phase of the proposed method. In order to evaluate the capability of the proposed method in dealing with difficult optimization problems, three real‐size building structures are considered, namely, a 10‐story building with 1,026 structural members, a 20‐story building with 3,860 members, and a 60‐story building with 8,272 members. The overall performance of the proposed quantum‐behaved developed swarm optimizer is compared with that of the standard developed swarm optimizer and other approaches. The obtained results proved that the proposed method is capable of providing better results for the considered examples than are the other algorithms.     

Network‐level scheduling of road construction projects considering user and business impacts

Construction projects are often associated with partial or full road closures, which result in user costs and community disruptions in terms of reduced business productivity. A number of studies have addressed the problem of scheduling construction projects based on a variety of stakeholder objectives. Yet still, there seems to exist a few gaps regarding (1) possible tradeoffs between road user cost reduction and business cost reduction associated with optimal scheduling, (2) role of the project type (rehabilitation and capacity expansion) on the solution methodology, and (3) lack of solution algorithm to address the problem complexity by deriving the optimal solution. In addressing these gaps, this article adopts a novel approach for developing an optimal project schedule for multiple road projects within a construction horizon. The goal is to minimize the overall cost of the projects to road users and adjacent businesses over the construction horizon. The project scheduling problem is formulated as a mixed‐integer nonlinear program. We solve the problem using a local decomposition method. The methodology is demonstrated using the Sioux Falls city network with two project types: capacity expansion and rehabilitation. The results of the numerical experiment suggest that (1) the solution algorithm converges to optimal solution in finite iterations and (2) a network‐wide scheduling of urban road projects using explicit optimization can yield a significant reduction in business disruption costs while incurring a relatively smaller increase in system travel time, and overall, is superior to a schedule developed only considering the total system travel time.     

Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment

To prevent bus bunching, a dynamic headway control method in the V2I (vehicle to infrastructure) environment for a high‐frequency route with bus lane is developed. Bus operating speed guidance on the mid‐blocks and intersection signal adjustment are two main strategies in the proposed method. A forecasting model of bus travel time under the dynamic control method is developed. The objective function is set up by taking into account differences between actual bus headways and dispatching headways, and the scaling ratios of intersection cycle lengths. The optimization model is solved using genetic algorithm. The proposed method is applied to a real bus route in Meihekou city, China, and compared with the current control plan as well as holding strategy. Results show that the proposed method can reduce bus headway deviations in all investigating periods; negative impacts on cars can be limited by setting reasonable values for the parameters.     

Development of a Data‐Driven Framework for Real‐Time Travel Time Prediction

Travel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long‐term prediction in a real‐time manner have been lacking. Existing methods do not fully utilize the advantages of the state‐of‐the‐art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real‐time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the long‐term (at least 6‐hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k‐nearest neighbor (Mk‐NN) method which is compared with the conventional k‐NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long‐term travel time with shorter computation time.