Actual Road Network Research Articles

Bridge-specific fragility analysis: when is it really necessary?

In seismic assessment of bridges the research focus has recently shifted on the derivation of bridge-specific fragility curves that account for the effect of different geometry, structural system, component and soil properties, on the seismic behaviour. In this context, a new, component-based methodology for the derivation of bridge-specific fragility curves has been recently proposed by the authors, with a view to overcoming the inherent difficulties in assessing all bridges of a road network and the drawbacks of existing methodologies, which use the same group of fragility curves for bridges within the same typological class. The main objective of this paper is to critically assess the necessity of bridge-specific fragility analysis, starting from the effect of structure-specific parameters on component capacity (limit state thresholds), seismic demand, and fragility curves. The aforementioned methodology is used to derive fragility curves for all bridges within an actual road network, with a view to investigating the consistency of adopting generic fragility curves for bridges that fall within the same class and quantifying the degree of over- or under-estimation of the probability of damage when generic bridge classes are considered. Moreover, fragility curves for all representative bridges of the analysed concrete bridge classes are presented to illustrate the differentiation in bridge fragility for varying structural systems, bridge geometry, total bridge length and maximum pier height. Based on the above, the relevance of bridge-specific fragility analysis is assessed, and pertinent conclusions are drawn.

Power-law distribution in an urban traffic flow simulation

We found power-law behavior in the distribution of traffic on road segments in urban traffic simulations using digitized map of Kobe city in Japan as an example of an actual road network. As a comparison, we performed simulations using an artificial random road network and Manhattan-type road network. Similar power-law behavior was confirmed in the former, but not the latter. The behavior appeared robustly with or without traffic congestion, which suggests that its origin is not the interaction between vehicles. The power-law exponent was fitted using least squares method and obtained as -1.1 for Kobe city and the random road network, with optimization to avoid traffic congestion. The result did not change with the use of a different origin and destination distribution. From these results, one of the reasons that caused the power-law behavior was considered to be the randomness of the road network connection and edge lengths, whose fluctuations are obvious both in Kobe city and the random road network, unlike the grid network.

Vehicle OD Matrix Acquisition and Dynamic Traffic Assignment Simulation Based on License Plate Data

In order to obtain the OD(Origin and Destination) matrix conveniently and accurately, the paper deals with the license plate data for time varying OD matrix between traffic crossroads by SPSS and Excel VBA, using vehicle trajectory tracking method to verify the reliability of the OD matrix. It provides an accurate and efficient OD investigation method for traffic planning and management department. Then, the obtained OD matrix is simulated by Vissim for dynamic traffic assignment study. It introduces the principle of dynamic traffic assignment based on Vissim, and combines the actual situation with Vissim platform to model actual road network and calibrate parameters. Finally, a network example result of the dynamic traffic assignment simulation is analyzed and evaluated. The overall delay of the overall network after DTA is reduced by 20%. The application of simulation results in road network evaluation and traffic planning management is also analyzed.

Multi-indicator Road Traffic State Assessment Based on Path State

In order to evaluate the traffic condition of urban road, the article makes an actual modeling of the urban road junctions and the analysis of the status of sub-lanes. Combined with various impedance factors of the road network, in this paper, a state coefficient matrix is used to represent the road network impedance data, and a road traffic status assessment system based on the actual path status with multi-parameter indexes is proposed. The evaluation system is improved based on the existing state assessment methods and breakthroughs in the assessment mode. In the past, it only estimated the overall traffic condition at the intersection; the evaluation method was more efficient and the evaluation result was more accurate. The actual road network in Shijingshan District is used to verify the results. The results show that the algorithm is effective.

S.Barisitz. Central asia and the silk road: economic rise and decline over several millennia. Studies in Economic History Series. Cham, Switzerland: Springer, 2017. 282+xv. ISBN: 978‐3‐319‐51212‐9.

The history of the Silk Road that links the East (theOrient, in particular China) and theWest (the Occident, in particular theMediterranean area) is a fascinating topic in premodern economic history. The Road is not an actual road but rather a network of potential geographical connections and its efficacy to function as a mode for transcontinental trade to a large extent depends on heterogeneous and changing institutional conditions. Clearly then, the Road can only truly connect when each of its heterogeneous network elements is reasonably safe and stable. An interesting aspect of the Road is that its history spans so many different cultures, civilizations, and empires. Therefore, changing conditions of time and place are associated with periods in which the Road is a stronger trade route and periods of relative decline. It continued reemergence time and again testifies the importance of historic conditions for international trade that echoes in the recent Chinese plans to create a China-centered trading network (theOne Road, One Belt initiative) that aims to increase connectivity of the Eurasian countries.

ExTCKNN: Expanding Tree-Based Continuous K Nearest Neighbor Query in Road Networks With Traffic Rules

The existing continuous nearest neighbor query algorithms of moving objects in road networks do not consider any traffic rule and assume that the speed of moving objects is constant and the topology of road networks never change. However, in real road networks, the object’s speed and the road network’s structure change frequently Hence, these would make the existing methods ineffective when applying to the real-world road network environment To overcome the aforementioned disadvantages, we propose a Data Modeling approach of Road Networks with traffic rules (called DMRNR) and design a novel Expanding Tree-based Continuous k Nearest Neighbors algorithm (abbreviate for ExTCKNN) that can be well adopted to the actual road network environment. The algorithm consists of three steps: 1) it obtains the query results to store using DMRNR in the initial phase; 2) it maintains the data model of road networks by monitoring the real-time change information; and 3) the results are generated according to the submitted query with the updated data model and the latest state of moving objects The merit of the proposed algorithm lies in that it queries the nearest neighbors by taking the movements of the moving object and the variety of the road networks into consideration Extensive experiments are conducted and the experimental results demonstrate a significant improvement of the proposed method when compared with conventional solutions.

Research of Intersection Groups Optimization Control Method Based on Critical Path Identification

An optimal intersection groups control method based on critical path identification is proposed. The control algorithm uses a binary search to gradually determine the scope of the intersection groups, uses the duality method to express the intersection groups, uses breadth-first search algorithm to solve the critical path, and finally uses the branch-and-bound method to solve the lane canalization, realizes the optimized output of phase-sequence and timing of intersection control signals. The control algorithm is realized using C++ language, including the following functional modules: intersection range dynamic definition, critical path identification, space-time resource optimization, and online timing adjustment of signal timing parameters and so on. Finally, the control algorithm is verified by the actual road network of Changzhou City of China, the result shows that, the efficiency of traffic operation is significantly improved.

Accurate and cost-effective traffic information acquisition using adaptive sampling: Centralized and V2V schemes

The new generation of GPS-based tolling systems allow for a much higher degree of road sensing than has been available up to now. We propose an adaptive sampling scheme to collect accurate real-time traffic information from large-scale implementations of on-board GPS-based devices over a road network. The goal of the system is to minimize the transmission costs over all vehicles while satisfying requirements in the accuracy and timeliness of the traffic information obtained. The system is designed to make use of cellular communication as well as leveraging additional technologies such as roadside units equipped with WiFi and vehicle-to-vehicle (V2V) dedicated short-range communications (DSRC). As opposed to fixed sampling schemes, which transmit at regular intervals, the sampling policy we propose is adaptive to the road network and the importance of the links that the vehicle traverses. Since cellular communications are costly, in the basic centralized scheme, the vehicle is not aware of the road conditions on the network. We extend the scheme to handle non-cellular communications via roadside units and vehicle-to-vehicle (V2V) communication. Under a general traffic model, we prove that our scheme always outperforms the baseline scheme in terms of transmission cost while satisfying accuracy and real-time requirements. Our analytical results are further supported via simulations based on actual road networks for both the centralized and V2V settings.

Cooperative Management in Full-Truckload and Less-Than-Truckload Vehicle System

In cooperative logistics, shippers can cooperate to improve efficiency and reduce the cost of product/package delivery services. In this paper, we consider the resource, i.e., vehicles, sharing problem to serve customers in the cooperative logistics environment. In the environment, small shippers cooperate by forming a coalition to create a vehicle pool and to allow sharing of their own vehicles with each other. The vehicles in the pool are used to deliver packages to customers. To minimize the delivery cost to the shippers, we propose a framework for the capacitated vehicle routing problem with time windows. The framework is composed of joint vehicle allocation, cost management, and overlapping coalition formation among shippers. For vehicle allocation, we formulate and solve optimization models to determine the number of vehicles and their delivery routes to meet customer demand and service time. The uncertainty of customer demand is taken into consideration. For sharing the delivery cost generated in the vehicle pool, i.e., cost management, among the cooperative shippers in a coalition, we apply the concept of Myerson value from the cooperative game theory as a solution. Based on the cost, the shippers then decide whether to cooperate and share the vehicles or not. The performance evaluation based on the test and actual road networks clearly shows the benefits of the proposed framework.

Prediction of electric vehicle charging-power demand in realistic urban traffic networks

This paper presents a time-spatial electric vehicle (EV) charging-power demand forecast model at fast-charging stations located in urban areas. Most previous studies have considered private charging locations and a fixed charging-start time to predict the EV charging-power demand. Few studies have considered predicting the EV charging-power demand in urban areas with time-spatial model analyses. The approaches used in previous studies also may not be applicable to predicting the EV charging-power demand in urban areas because of the complicated urban road network. To possibly forecast the actual EV charging-power demand in an urban area, real-time closed-circuit television (CCTV) data from an actual urban road network are considered. In this study, a road network inside the metropolitan area of Seoul, South Korea was used to formulate the EV charging-power demand model using two steps. First, the arrival rate of EVs at the charging stations located near road segments of the urban road network is determined by a Markov-chain traffic model and a teleportation approach. Then, the EV charging-power demand at the public fast-charging stations is determined using the information from the first step. Numerical examples for the EV charging-power demand during three time ranges (i.e., morning, afternoon, and evening) are presented to predict the charging-power demand profiles at the public fast-charging stations in urban areas. The proposed time-spatial model can also contribute to investment and operation plans for adaptive EV charging infrastructures with renewable resources and energy storage depending on the EV charging-power demand in urban areas.

Practical approach to determining traffic congestion propagation boundary due to traffic incidents

A practical approach for predicting the congestion boundary due to traffic incidents was proposed. Based on the kinematic wave theory and Van Aerde single-regime flow model, a model for estimating the congestion propagation speed for the basic road segment was developed. Historical traffic flow data were used to analyze the time variant characteristics of the urban traffic flow for each road type. Then, the saturation flow rate was used for analyzing the impact of the traffic incident on the traversing traffic flow at the congestion area. The base congestion propagation speed for each road type was calculated based on field data, which were provided by the remote traffic microwave sensors (RTMS), floating car data (FCD) system and screen line survey. According to a comparative analysis of the congestion propagation speed, it is found that the expressway, major arterial, minor arterial and collector are decreasingly influenced by the traffic incident. Subsequently, the impact of turning movements at intersections on the congestion propagation was considered. The turning ratio was adopted to represent the impact of turning movements, and afterward the corresponding propagation pattern at intersections was analyzed. Finally, an implementation system was designed on a geographic information system (GIS) platform to display the characteristics of the congestion propagation over the network. The validation results show that the proposed approach is able to capture the congestion propagation properties in the actual road network.

Measuring Road Network Vulnerability with Sensitivity Analysis.

This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery.

Average weighted receiving time on the non-homogeneous double-weighted fractal networks

In this paper, based on actual road networks, a model of the non-homogeneous double-weighted fractal networks is introduced depending on the number of copies s and two kinds of weight factors wi,ri(i=1,2,…,s). The double-weights represent the capacity-flowing weights and the cost-traveling weights, respectively. Denote by wijF the capacity-flowing weight connecting the nodes i and j, and denote by wijC the cost-traveling weight connecting the nodes i and j. Let wijF be related to the weight factors w1,w2,…,ws, and let wijC be related to the weight factors r1,r2,…,rs. Assuming that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the capacity-flowing weight of edge linking them. The weighted time for two adjacency nodes is the cost-traveling weight connecting the two nodes. The average weighted receiving time (AWRT) is defined on the non-homogeneous double-weighted fractal networks. AWRT depends on the relationships of the number of copies s and two kinds of weight factors wi,ri(i=1,2,…,s). The obtained remarkable results display that in the large network, the AWRT grows as a power-law function of the network size Ng with the exponent, represented by θ=logs(w1r1+w2r2+⋯+wsrs)<1 when w1r1+w2r2+⋯+wsrs≠1, which means that the smaller the value of w1r1+w2r2+⋯+wsrs is, the more efficient the process of receiving information is. Especially when w1r1+w2r2+⋯+wsrs=1, AWRT grows with increasing order Ng as logNg or (logNg)2 . In the classic fractal networks, the average receiving time (ART) grows with linearly with the network size Ng. Thus, the non-homogeneous double-weighted fractal networks are more efficient than classic fractal networks in term of receiving information.

Accurate and Cost-Effective Traffic Information Acquisition using Adaptive Sampling: Centralized and V2V Schemes

Abstract: The new generation of GPS-based tolling systems allow for a much higher degree of road sensing than has been available up to now. We propose an adaptive sampling scheme to collect accurate real-time traffic information from large-scale implementations of on-board GPS-based devices over a road network. The goal of the system is to minimize the transmission costs over all vehicles while satisfying requirements in the accuracy and timeliness of the traffic information obtained. The system is designed to make use of cellular communication as well as leveraging additional technologies such as roadside units equipped with WiFi and vehicle-to-vehicle (V2V) dedicated short-range communications (DSRC). As opposed to fixed sampling schemes, which transmit at regular intervals, the sampling policy we propose is adaptive to the road network and the importance of the links that the vehicle traverses. Since cellular communications are costly, in the basic centralized scheme, the vehicle is not aware of the road conditions on the network. We extend the scheme to handle non-cellular communications via roadside units and vehicle-to-vehicle (V2V) communication. Under a general traffic model, we prove that our scheme always outperforms the baseline scheme in terms of transmission cost while satisfying accuracy and real-time requirements. Our analytical results are further supported via simulations based on actual road networks for both the centralized and V2V settings.

A quantum particle swarm optimization driven urban traffic light scheduling model

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases.

Solving the stochastic time-dependent orienteering problem with time windows

This paper introduces the stochastic time-dependent orienteering problem with time windows. The orienteering problem occurs in logistic situations where an optimal combination of locations must first be selected and then the routing between these selected locations must be optimized. In the stochastic time-dependent variant, the travel time between two locations is a stochastic function that depends on the departure time at the first location. The main contribution of this paper lies in the design of a fast and effective algorithm to solve this challenging problem. To validate the performance and the practical relevance of this proposed algorithm, several experiments were carried out on realistic benchmark instances of varying size and properties. These benchmark instances are constructed based on an actual large road network in Belgium with historic travel time profiles for every road segment.

A Bi‐objective Optimization Model for Designing Safe Walking Routes for School Children

This study uses a mathematical optimization approach to design safe walking routes from school to home for children. Children are thought to be safer when walking together in groups rather than alone. Thus, we assume that the risk of walking along a given road segment in a group is smaller than that of walking the same segment alone. At the same time, the walking route between school and home for each child should not deviate substantially from the shortest route. We propose a bi‐objective model that minimizes both the total risk (particularly, the total distance walked alone) and the total walking distance for all children. We present an integer programming formulation of the proposed problem and apply this formulation to two instances based on actual road networks. We obtain Pareto optimal solutions using a mathematical programming solver and analyze the characteristics of the solutions and their potential applicability to real situations. The results show that the proposed model produces much better solutions compared with the solution where each child walks along the shortest path from school to home. In some optimal solutions, only a small deviation from the shortest path results in a dramatic reduction of the risk objective.

The Impact of Short Term Traffic Forecasting on the Effectiveness of Vehicles Routes Planning in Urban Areas

An impossibility to foresee in advance the accurate traffic parameters in face of dynamism phenomena in complex transportation system is a one of the major source of uncertainty. The paper presents an approach to robust optimization of logistics vehicle routes in urban areas on the basis of estimated short-term traffic time forecasts in a selected area of the urban road network. The forecast values of optimization parameters have been determined using the spectral analysis model, taking into account the forecast uncertainty degree. The robust counterparts approach of uncertain bi-criteria shortest path problem formulation is used to determining the robust routes for logistics vehicles in the urban network. The uncertainty set is created on the basis of forecast travel times in chosen sections, estimated by means of spectral analysis. The advantages and the characteristics are exemplified in the actual Krakow road network. The obtained data have been compared with classic approach wherein it is assumed that the optimization parameters are certain and accurate. The results obtained in the simulation example indicate that use of forecasting techniques with robust optimization models has a positive impact on the quality of final solutions.

Parallel Hybrid Genetic Algorithm and GIS-Based Optimization for Municipal Solid Waste Collection Routing

AbstractA vast majority of studies on municipal solid waste (MSW) collection routing do not consider the constraints pertinent to actual road networks such as unidirectional roads and terrain chara...

SCALING OF AVERAGE WEIGHTED RECEIVING TIME ON DOUBLE-WEIGHTED KOCH NETWORKS

In this paper, we introduce a model of the double-weighted Koch networks based on actual road networks depending on the two weight factors w,r ∈ (0, 1]. The double weights represent the capacity-flowing weight and the cost-traveling weight, respectively. Denote by [Formula: see text] the capacity-flowing weight connecting the nodes i and j, and denote by [Formula: see text] the cost-traveling weight connecting the nodes i and j. Let [Formula: see text] be related to the weight factor w, and let [Formula: see text] be related to the weight factor r. This paper assumes that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the capacity-flowing weight of edge linking them. The weighted time for two adjacency nodes is the cost-traveling weight connecting the two nodes. We define the average weighted receiving time (AWRT) on the double-weighted Koch networks. The obtained result displays that in the large network, the AWRT grows as power-law function of the network order with the exponent, represented by θ(w,r) = ½ log2(1 + 3wr). We show that the AWRT exhibits a sublinear or linear dependence on network order. Thus, the double-weighted Koch networks are more efficient than classic Koch networks in receiving information.