Real-world Road Networks Research Articles

Nonconvex <inline-formula> <tex-math notation="LaTeX">$l_p$ </tex-math> </inline-formula>-Norm Regularized Sparse Self-Representation for Traffic Sensor Data Recovery

Recovering missing values from incomplete traffic sensor data is an important task for intelligent transportation system because most algorithms require data with complete entries as input. Self-representation-based matrix completion attempts to optimally represent each sample by linearly combining other samples when conducting missing values recovery. Typically, it implements sparse or dense combination through imposing either $l_{1}$ -norm or $l_{2}$ -norm regularization over the representation coefficients, which is not always optimal in practice. To permit more flexibility, we propose in this paper a novel approach termed as $l_{p}$ -norm regularized sparse self-representation (SSR- $l_{p}$ ) by incorporating nonconvex $l_{p}$ -norm with $0 as regularization. In such a way, it is able to produce more sparsity than $l_{1}$ -norm and in turn facilitates the accurate recovery of missing data. We further develop an efficient iterative algorithm for solving SSR- $l_{p}$ . The performance of this method is evaluated on a real-world road network traffic flow data set. The experimental results verify the advantage of our method over other competing algorithms in recovering missing values.

Coping with Large Traffic Volumes in Schedule-Driven Traffic Signal Control

Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to significantly improve traffic flow efficiency in complex urban road networks. However, in situations where vehicle volumes increase to the point that the physical capacity of a road network reaches or exceeds saturation, it has been observed that the effectiveness of a schedule-driven approach begins to degrade, leading to progressively higher network congestion. In essence, the traffic control problem becomes less of a scheduling problem and more of a queue management problem in this circumstance. In this paper we propose a composite approach to real-time traffic control that uses sensed information on queue lengths to influence scheduling decisions and gracefully shift the signal control strategy to queue management in high volume/high congestion settings. Specifically, queue-length information is used to establish weights for the sensed vehicle clusters that must be scheduled through a given intersection at any point, and hence bias the wait time minimization calculation. To compute these weights, we develop a model in which successive movement phases are viewed as different states of an Ising model, and parameters quantify strength of interactions. To ensure scalability, queue information is only exchanged between direct neighbors and the asynchronous nature of local intersection scheduling is preserved. We demonstrate the potential of the approach through microscopic traffic simulation of a real-world road network, showing a 60% reduction in average wait times over the baseline schedule-driven approach in heavy traffic scenarios. We also report initial field test results, which show the ability to reduce queues during heavy traffic periods.

Optimal planning of swapping/charging station network with customer satisfaction

Key to the mass adoption of electric vehicles is the establishment of a sufficient battery service infrastructure network on the basis of customer behavior and psychology. Motivated by EV service infrastructure network design under the battery leasing/electric car sharing service business models, we present an electric vehicle battery service network design problem considering a customers satisfaction related to “range anxiety” and “loss anxiety”. The problem is formulated as a linear integer programming model under deterministic and fuzzy scenarios. A Tabu Search heuristic combined with GRASP is proposed to efficiently solve the problem. Finally, we conduct parametric analysis on real-world road networks.

A dual approximation-based quantum-inspired genetic algorithm for the dynamic network design problem

In this paper, we formulate a dynamic transportation network design model in which traffic dynamics are modeled by the cell transmission model. In the formulation, transportation planners decide on the optimal capacity expansion policies of existing transportation network infrastructure with limited resources, while road users react to the capacity changes by selfishly choosing routes to maximize their own profit. Owing to the problem complexity, a majority of the research efforts have focused on tackling this problem using meta-heuristics. In this study, we incorporate a series of dual-variable approximation techniques into the paradigm of a quantum-inspired genetic algorithm (QIGA) and devise an efficient evaluation function based on these techniques. The proposed QIGA contains a series of enhancements compared to conventional genetic algorithms (GAs) and can be considered as a better alternative when solving problems with a complex solution space. The QIGA is applied to a synthetic network, a subnetwork of a real-world road network, and a realistic network to justify its theoretical and practical value. From the numerical results, it is found that in the same computational time, the QIGA outperforms the conventional GA by 3.86–5.63% in terms of the objective value, which can be significant, especially when network expansion of a large urban area is considered. Technical, computational, and practical issues involved in developing the QIGA are investigated and discussed.

A risk-averse signal setting policy for regulating hazardous material transportation under uncertain travel demand

To effectively regulate risk associated with hazmat transportation and minimize travel cost over road network under uncertain travel demand, a min-max bi-level programming problem (MMBLPP) is proposed to determine traffic signal settings. A risk-averse signal setting policy using budget of uncertainty (BOU) is presented against the worst-case realization of uncertainty in travel demand. A risk-averse model determining system optimum routes for hazmat carriers is presented where travel delay incurred by regular traffic at downstream junctions has been taken into account. A min-max single-level model (MMX) and a bundle-like solution method are proposed to determine risk-averse signal settings under BOU-based travel demand. To investigate the effectiveness of proposed model, numerical evaluations using real-world road network are empirically made with various scenarios of transport policies. These results reported obviously indicate that the proposed model achieved relatively better results than did traditional one against high-consequence scenarios of uncertain travel demand while substantially mitigating risk in the worst cases.

Cost-Optimal Time-dEpendent Routing with Time and Speed Constraints in Directed Acyclic Road Networks

Travel costs on road networks always change over time which implies road networks are time dependent. Most studies on time-dependent road networks simply find the shortest path with the least travel time without considering waiting at some nodes, or fuel consumption and toll fee. In real-world applications or computer games, waiting may be allowed at some nodes but disallowed at other nodes; a user can traverse an edge at different speeds; monetary travel cost contains fuel cost and toll fees; and users usually prefer the minimum-cost route under time and speed constraints. Therefore, we study Cost-Optimal Time-dEpendent Routing (COTER) problem with time and speed constraints. We utilize two fuel consumption models and compute the minimum fuel consumption with given travel time for highway edges via nonlinear optimization. We allow the toll fee function to be an arbitrary single-valued time-dependent function. We define an Optimal Cost (OC) function for each candidate node [Formula: see text], and derive the recurrence relation formula between [Formula: see text]’s incoming neighbors’ OC-functions and [Formula: see text]’s OC-functions. To solve COTER, we propose a five-step algorithm, namely, ALG-COTER, which uses Fibonacci-heap optimized Dijkstra, topological sorting, dynamic programming, binary min-heap optimization, nonlinear optimization, and backtracking algorithms. Experimental results on three real-world road networks of different sizes demonstrate that our algorithm finds the optimal route efficiently and is scalable to different parameters.

A Continuous Representation of Ad Hoc Ridesharing Potential

Interacting with ridesharing systems is a complex spatiotemporal task. Traditional approaches rely on the full disclosure of a client's trip information to perform ride matching. However during poor service conditions of low supply or high demand, this requirement may mean that a client cannot find any ride matching their intentions. To address this within real-world road networks, we extend our map-based opportunistic client user interface concept, i.e., launch pads, from a discrete to a continuous space–time representation of vehicle accessibility to provide a client with a more realistic choice set. To examine this extension under different conditions, we conduct two computational experiments. First, we extend our previous investigation into the effects of varying vehicle flexibility and population size on launch pads and a client's probability of pick-up, describing the increased opportunity. Second, observing launch pads within a real-world road network, we analyze aspects of choice and propose necessary architecture improvements. The communication of ride share potential using launch pads provides a client with a simple yet flexible means of interfacing with on-demand transportation.

Three-stage approach for dynamic traffic temporal-spatial model

In order to describe the characteristics of dynamic traffic flow and improve the robustness of its multiple applications, a dynamic traffic temporal-spatial model (DTTS) is established. With consideration of the temporal correlation, spatial correlation and historical correlation, a basic DTTS model is built. And a three-stage approach is put forward for the simplification and calibration of the basic DTTS model. Through critical sections pre-selection and critical time pre-selection, the first stage reduces the variable number of the basic DTTS model. In the second stage, variable coefficient calibration is implemented based on basic model simplification and stepwise regression analysis. Aimed at dynamic noise estimation, the characteristics of noise are summarized and an extreme learning machine is presented in the third stage. A case study based on a real-world road network in Beijing, China, is carried out to test the efficiency and applicability of proposed DTTS model and the three-stage approach.

Memetic algorithm with route decomposing for periodic capacitated arc routing problem

In this paper, the Periodic Capacitated Arc Routing Problem (PCARP) is investigated. PCARP is an extension of the well-known CARP from a single period to a multi-period horizon. In PCARP, two objectives are to be minimized. One is the number of required vehicles (nv), and the other is the total cost (tc). Due to the multi-period nature, given the same graph or road network, PCARP can have a much larger solution space than the single-period CARP counterpart. Furthermore, PCARP consists of an additional allocation sub-problem (of the days to serve the arcs), which is interdependent with the routing sub-problem. Although some attempts have been made for solving PCARP, more investigations are yet to be done to further improve their performance especially on large-scale problem instances. It has been shown that optimizing nv and tc separately (hierarchically) is a good way of dealing with the two objectives. In this paper, we further improve this strategy and propose a new Route Decomposition (RD) operator thereby. Then, the RD operator is integrated into a Memetic Algorithm (MA) framework for PCARP, in which novel crossover and local search operators are designed accordingly. In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals. The MA with RD (MARD) was evaluated and compared with the state-of-the-art approaches on two benchmark sets of PCARP instances and a large data set which is based on a real-world road network. The experimental results suggest that MARD outperforms the compared state-of-the-art algorithms, and improves most of the best-known solutions. The advantage of MARD becomes more obvious when the problem size increases. Thus, MARD is particularly effective in solving large-scale PCARP instances. Moreover, the efficacy of the proposed RD operator in MARD has been empirically verified.

Spatial-Temporal Congestion Identification Based on Time Series Similarity Considering Missing Data

Traffic congestion varies spatially and temporally. The observation of the formation, propagation and dispersion of network traffic congestion can lead to insights about the network performance, the bottleneck dynamics etc. While many researchers use the traffic flow data to reconstruct the congestion profile, the data missing problem is bypassed. Current methods either omit the missing data or supplement the missing part by average etc. Great error may be introduced during these processes. Rather than simply discarding the missing data, this research regards the data missing event as a result of either the severe congestion which prevent the floating vehicle from entering the congested area, or a type of feature of the resulting traffic flow time series. Hence a new traffic flow operational index time series similarity measurement is expected to be established as a basis of identifying the dynamic network bottleneck. The method first measures the traffic flow operational similarity between pairs of neighboring links, and then the similarity results are used to cluster the spatial-temporal congestion. In order to get the similarity under missing data condition, the measurement is implemented in a two-stage manner: firstly the so called first order similarity is calculated given that the traffic flow variables are bounded both upside and downside; then the first order similarity is aggregated to generate the second order similarity as the output. We implement the method on part of the real-world road network; the results generated are not only consistent with empirical observation, but also provide useful insights.

Access Time Oracle for Planar Graphs

The study of urban networks reveals that the accessibility of important city objects for the vehicle traffic and pedestrians is significantly correlated to the popularity, micro-criminality, micro-economic vitality, and social liveability of the city, and is always the chief factor in regulating the growth and expansion of the city. The accessibility between different components of an urban structure are frequently measured along the streets and routes considered as edges of a planar graph, while the traffic ultimate destination points and street junctions are treated as vertices. For estimation of the accessibility of destination vertex $j$ from vertex $i$ through urban networks, in particular, the random walks are used to calculate the expected distance a random walker starting from $i$ makes before $j$ is visited (known as access time ). The state-of-the-art of access time computation is costly in large planar graphs since it involves matrix operation over entire graph. The time complexity is $O(n^{2.376})$ where $n$ is the number of vertices in the planar graph. To enable efficient access time query answering in large planar graphs, this work proposes the first access time oracle which is based on the proposed access time decomposition and reconstruction scheme. The oracle is a hierarchical data structure with deliberate design on the relationships between different hierarchical levels. The storage requirement of the proposed oracle is $O(n^{\frac{4}{3}}\log \log n)$ and the access time query response time is $O(n^{\frac{2}{3}})$ . The extensive tests on a number of large real-world road networks (with up to about 2 million vertices) have verified the superiority of the proposed oracle.

Optimal Multi-Meeting-Point Route Search

Real-time ride-sharing applications (e.g., Uber and Lyft) are very popular in recent years. Motivated by the ride-sharing application, we propose a new type of query in road networks, called the optimal multi-meeting-point route ( $\mathsf {OMMPR}$ ) query. Given a road network $G$ , a source node $s$ , a target node $t$ , and a set of query nodes $U$ , the $\mathsf {OMMPR}$ query aims at finding the best route starting from $s$ and ending at $t$ such that the weighted average cost between the cost of the route and the total cost of the shortest paths from every query node to the route is minimized. We show that the problem of computing the $\mathsf {OMMPR}$ query is NP-hard. To answer the $\mathsf {OMMPR}$ query efficiently, we propose two novel parameterized solutions based on dynamic programming (DP), with the number of query nodes $l$ (i.e., $l=|U|$ ) as a parameter, which is typically very small in practice. The two proposed parameterized algorithms run in $O(3^l\cdot m+ 2^l \cdot n\cdot (l+\log (n)))$ and $O(2^l \cdot (m + n\cdot (l+\log (n))))$ time, respectively, where $n$ and $m$ denote the number of nodes and edges in graph $G$ , thus they are tractable in practice. To reduce the search space of the DP-based algorithms, we propose two novel optimized algorithms based on bidirectional DP and a carefully-designed lower bounding technique. We conduct extensive experimental studies on four large real-world road networks, and the results demonstrate the efficiency of the proposed algorithms.

A Pruning Algorithm for Reverse Nearest Neighbors in Directed Road Networks

In this paper, we studied the problem of reverse k nearest neighbors (RkNN) in directed road network, where a road segment can have a particular orientation. A RNN query returns a set of data objects that take query point as their nearest neighbor. Although, much research has been done for RNN in Euclidean and undirected network space, very less attention has been paid to directed road network, where network distances are not symmetric. In this paper, we provided pruning rules which are used to minimize the network expansion while searching for the result of a RNN query. Based on these pruning rules we provide an algorithm named SWIFT for answering RNN queries in static directed road network. We evaluated SWIFT on a real world road network and our experimental results show that SWIFT significantly outperforms the naive algorithm in terms of computational cost.

Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

This paper focuses on the problems of short-term traffic flow forecasting. The main goal is to put forward traffic correlation model and real-time correction algorithm for traffic flow forecasting. Traffic correlation model is established based on the temporal-spatial-historical correlation characteristic of traffic big data. In order to simplify the traffic correlation model, this paper presents correction coefficients optimization algorithm. Considering multistate characteristic of traffic big data, a dynamic part is added to traffic correlation model. Real-time correction algorithm based on Fuzzy Neural Network is presented to overcome the nonlinear mapping problems. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling methods.

Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

Authentication of k Nearest Neighbor Query on Road Networks

Outsourcing spatial databases to the cloud provides an economical and flexible way for data owners to deliver spatial data to users of location-based services. However, in the database outsourcing paradigm, the third-party service provider is not always trustworthy, therefore, ensuring spatial query integrity is critical. In this paper, we propose an efficient road network k-nearest-neighbor query verification technique which utilizes the network Voronoi diagram and neighbors to prove the integrity of query results. Unlike previous work that verifies k-nearest-neighbor results in the Euclidean space, our approach needs to verify both the distances and the shortest paths from the query point to its kNN results on the road network. We evaluate our approach on real-world road networks together with both real and synthetic points of interest datasets. Our experiments run on Google Android mobile devices which communicate with the service provider through wireless connections. The experiment results show that our approach leads to compact verification objects (VO) and the verification algorithm on mobile devices is efficient, especially for queries with low selectivity.

On k-path covers and their applications

For a directed graph G with vertex set V we call a subset C ⊆ V a k-(All-)Path Cover if C contains a node from any path consisting of k nodes. This paper considers the problem of constructing small k -Path Covers in the context of road networks with millions of nodes and edges. In many application scenarios the set C and its induced overlay graph constitute a very compact synopsis of G which is the basis for the currently fastest data structure for personalized shortest path queries, visually pleasing overlays of subsampled paths, and efficient reporting, retrieval and aggregation of associated data in spatial network databases. Apart from a theoretical investigation of the problem, we provide efficient algorithms that produce very small k -Path Covers for large real-world road networks (with a posteriori guarantees via instance-based lower bounds).

Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem

In urban areas, logistic transportation operations often run into problems because travel speeds change, depending on the current traffic situation. If not accounted for, time-dependent and stochastic travel speeds frequently lead to missed time windows and thus poorer service. Especially in the case of passenger transportation, it often leads to excessive passenger ride times as well. Therefore, time-dependent and stochastic influences on travel speeds are relevant for finding feasible and reliable solutions. This study considers the effect of exploiting statistical information available about historical accidents, using stochastic solution approaches for the dynamic dial-a-ride problem (dynamic DARP). The authors propose two pairs of metaheuristic solution approaches, each consisting of a deterministic method (average time-dependent travel speeds for planning) and its corresponding stochastic version (exploiting stochastic information while planning). The results, using test instances with up to 762 requests based on a real-world road network, show that in certain conditions, exploiting stochastic information about travel speeds leads to significant improvements over deterministic approaches.

Fully dynamic update of arc-flags

Best connections in real networks are usually found by applying Dijkstra's shortest paths algorithm. Unfortunately, networks deriving from real-world applications are huge, yielding unsustainable times to compute shortest paths. Therefore, considerable research has been conducted in recent years to accelerate Dijkstra's algorithm on typical instances of transportation and communication networks, such as road networks. These efforts have led to the development of many so called speed-up techniques, as for example Arc-Flags. The main drawback of many of these techniques, including Arc-Flags, is that they do not work well in the realistic dynamic scenarios where the networks change over time. In this article, we introduce a new data structure, named Road-Signs, which is used to update the Arc-Flags of a graph in fully dynamic scenarios. Road-Signs can be used to compute Arc-Flags, can be efficiently updated and does not require large space consumption for sparse networks. We develop a fully dynamic algorithm for updating Arc-Flags, by updating Road-Signs, each time that a modification occurs on an edge of the network. We show that this algorithm is better than recomputation from scratch of Arc-Flags in terms of the affected parameters of the input, which makes this solution suitable to be efficient in practice. However, it is not better than recomputation from scratch in the worst case. We also propose an experimental study to evaluate the practical performance of the new dynamic algorithm. To this aim, we use real-world road networks subject to sequences of weight change operations. Our experiments show a significant speed-up in the updating phase with respect to the recomputation from scratch of Arc-Flags.Copyright © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 633, 243-259 2014

Optimal Bus Lane Infrastructure Design

The study referred to in this paper focused on the problem of bus lane infrastructure design in a given road network. The main goal was to seek optimal planning (bus lane infrastructure layout) and operational strategies (such as bus frequency) in an integrated manner. The decision process involved three parties: the infrastructure planner who designed bus lane infrastructure from a public perspective, transit enterprises that operated buses on the given infrastructure from a business perspective, and travelers who determined their own routes from an individual perspective. The problem was formulated as a trilevel mathematical program, with each level corresponding to one party in the system. A heuristic solution algorithm combining a genetic algorithm and simulated annealing was developed to solve the proposed model efficiently. A case study based on a real-world road network in Beijing was implemented to test the efficiency and applicability of the proposed modeling and computing methods.