Real Traffic Network Research Articles

Traffic speed prediction of regional complex road network based on CapsNet and D-BiLSTM

Accurate and efficient short-term traffic prediction is of great significance in the study of regional traffic network. However, the complex and dynamic spatiotemporal correlation of traffic patterns makes the existing methods insufficient in learning traffic evolution in terms of structural depth and prediction scale. Therefore, this paper proposes a deep learning model combining capsule network (CapsNet) and deep bidirectional LSTM (D-BiLSTM). CapsNet is used to identify the spatial topological structure of the road network and extract spatial features, and D-BiLSTM network is integrated. The forward and backward dependencies of traffic states are considered at the same time, and the bidirectional temporal correlation of different historical periods is captured to predict the traffic of large-scale complex road networks in the target area. Experiments on real traffic network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average , which is better than other methods. It has high prediction accuracy and good robustness in traffic prediction of regional complex road networks.

Abnormal cascading dynamics in transportation networks based on Gaussian distribution of load

In the transportation network, we observe that the distance people travel by means of transportation follows a certain distribution. Statistical analysis shows that people’s travel distance is mainly concentrated in a medium range by the same vehicle, and they choose fewer destinations that are extremely close or far away. However, in previous studies, the impact of distance on the distribution of load flow within the network has often been neglected, or at best, addressed with overly simplistic assumptions. Therefore, we quantify the load flow distribution based on the Gaussian distribution of distances between the nodes. On this basis, a new cascading failure model is proposed using the shortest path strategy to calculate the initial load of the edge. Through the simulation of three real traffic networks and two artificially constructed networks with similar structural characteristics of traffic networks, we found the following interesting anomalies: First, increasing the load-bearing capacity of edges within the network does not necessarily lead to enhanced robustness. Second, we observed that removing more edges does not necessarily lead to a decrease in network robustness; conversely, the network robustness can be higher when a moderate number of edges are removed compared to fewer edges. To better understand the two anomalous dynamics phenomena we observed, we ran simulations on a small-scale network extracted from a real traffic network. We found that, under certain circumstances, the premature failure of some edges may isolate certain regions from the network, which may be responsible for this paradox.

SRNN-RSA: a new method to solving time-dependent shortest path problems based on structural recurrent neural network and ripple spreading algorithm

AbstractInfluenced by external factors, the speed of vehicles in the traffic network is changing all the time, which makes the traditional static shortest route unable to meet the real logistics distribution needs. Considering that the existing research on time-dependent shortest path problems (TDSPP) do not include the topological information of the traffic network, it is unable to reflect the spatial and temporal dynamic characteristics of the traffic network during the vehicle travelling process and is unable to update to the changes of the vehicle speed in real time, and poor scalability. Therefore, we used the structural RNN (SRNN) model containing topological information of the road network is used to predict time-varying speeds in the traffic road network. We proposed an SRNN-RSA framework for solving the TDSPP problem, which achieves a synergistic evolution between the real-time vehicle speed change process and the RSA solving process, and the scalability of the proposed SRNN-RSA is demonstrated and validated using different real data. Compared with other algorithms, the results show that SRNN-RSA has the lowest error with the actual situation, which can balance the solution accuracy and calculation speed and is more consistent with the real traffic road network, with better stability and expandability.

Assessment of future parking systems with autonomous vehicles through agent-based simulation: A case study of Hangzhou, China

Autonomous vehicles (AVs) are expected to transform urban parking. AVs may not require dedicated parking spaces near their destination. Instead, AVs can drive to the optimal parking lot, cruise, or return to the origin after the passenger gets off. Previous research on AV parking strategies has largely overlooked actual trip patterns and road network conditions that can affect the effectiveness and feasibility of these strategies. In this paper, we construct an agent-based simulation model to assess the potential impacts of AVs on urban parking, focusing on new AV parking strategies. Using the real traffic network and trip data of Hangzhou, we assess the impacts on overall network efficiency, personal trip, and parking demand under different AV penetration rates. Results show that AVs could decrease the total parking cost from 23 to 6 RMB and increase parking convenience for both AVs and human-driven vehicles (HDVs). 21% of AVs will still choose to park, while 70% will return to the origin. On average, each trip with AV will generate an additional zero-occupant distance of 8.8 km, exacerbating the congestion. The findings indicate the benefits and challenges of new AV parking strategies, providing valuable insights for future urban parking planning and policy making.

Using data to develop cost-effective battery-swap networks for electric vehicles

Electric vehicle (EV) development faces the challenges of range anxiety and the high construction and operating costs of charging and battery-swap infrastructure. To address these issues, this paper describes a proposed battery-swap network (BSN) comprising battery-swapping van service points and battery-swap stations to provide local, fast and cost-effective battery-swapping services. A data-driven approach was developed to analyse and model vehicle trajectory data to obtain realistic battery-swap demand distributions. An optimisation model based on location cover was then developed to minimise the cost of a BSN using a greedy algorithm. Real traffic network driving distance was adopted to improve authenticity and accuracy. In a case study, a BSN used a 1 week trajectory data set of taxis in Beijing, China and tested the effects in eight scenarios. The results showed that the mesh size of service points, service radius and coverage rate all had an impact on service quality and cost. It is concluded that a BSN could be cost-effectively deployed by adjusting these parameters to suit the needs of EVs at different development stages. This paper provides a useful reference for decision makers and operators to formulate policies and strategies.

Discharge control policy based on density and speed for deep Q-learning adaptive traffic signal

This study introduces a control strategy based on intersection capacity. The optimisation technique is formulated from available space at discharge routes. The downstream policy utilises density and speed (k-v) measurements to guide a deep Q-learning agent (DQLA) in managing a signalised junction using a constrained local communication protocol. Testing of the DQLA k-v strategy against other control methods is carried out in a simulated micro-model of a real urban traffic network. Though the adaptive signal system design is decentralised, statistical analyses explicitly prove the effectiveness of the discharge-based controller in mitigating operation at a global scale. The DQLA k-v controller has achieved significant cost savings in waiting time (10%−36%) and travel time (5%−25%) and asserted the highest mean travel speed (3.4 m/s). Consequently, vehicular traffic experienced the least time loss when traversing routes and witnessed fewer stops leading to close to optimum network operation at a 0.80 clearance ratio.

Metamodel-Based Optimization Method for Traffic Network Signal Design under Stochastic Demand

Traffic network design problems (NDPs) play an important role in urban planning. Since there exist uncertainties in the real urban traffic network, neglecting the uncertainty factors may lead to unreasonable decisions. This paper considers the transportation network signal design problem under stochastic origin-destination (OD) demand. In general, solving this stochastic problem requires a large amount of computational budget to calculate the equilibrium flow corresponding to a certain demand distribution, which limits its real applications. To reduce the computational time in calculating the equilibrium flow under stochastic demand, this paper proposes a metamodel-based optimization method. First, a combined metamodel that integrates a physical modeling part and a model bias generic part is developed. The metamodel is used to approximate the time-consuming average equilibrium flow solution process, hence to improve the computational efficiency. To further improve the convergence and the solution optimality performance of the metamodel-based optimization, the gradient information of traffic flow with respect to the signal plan is incorporated in the optimization model. A gradient-based metamodel algorithm is then proposed. In the numerical example, a six-node test network is used to examine the proposed metamodel-based optimization method. The proposed combined metamodel is compared with the benchmark method to investigate the importance of incorporating a model bias generic part and the traffic flow gradient information in the combined metamodel. Although there is a reduction in solution optimality since the metamodel is an approximation of the original model, the metamodel methods greatly improve the computational efficiency (the computational time is reduced by 4.84 to 13.47 times in the cases of different initial points). By incorporating the model bias, the combined metamodel can better approximate the original optimal solution. Moreover, incorporating the gradient information of the traffic flow in the optimization search algorithm can further improve the solution performance. Numerical results show that the gradient-based metamodel method can effectively improve the computation efficiency while slightly reducing the solution optimality (with an increase of 0.09% in the expected total travel cost).

Optimal Routing and Charging of Electric Logistics VehiclesBased on Long-Distance Transportation and Dynamic Transportation System

The application of electric vehicles (EVs) in the logistics industry has become more extensive. However, the mileage limitation of electric logistics vehicles (ELVs) and the long-distance distribution of ELVs have become urgent problems. Therefore, this paper proposes a long-distance distribution model for ELVs based on dynamic traffic information considering fleet mileage, distribution time and total distribution cost as the optimisation objectives, thus reasonably planning road selection and charging, and alleviating “mileage anxiety” in the long-distance distribution of ELVs. The model proposed in this paper comprehensively considers the characteristics of the high-speed and low-speed roads, the changes in road traffic flow on weekdays and non-weekdays, the time-of-use electricity price of electric vehicle charging stations (EVCSs) and uses the M/M/s queuing theory model to determine the charging waiting time. Finally, a real traffic network is taken as an example to verify the practicability and effectiveness of this model.

Electric vehicle navigation and cluster dispatch for reliable low-carbon traffic–Power systems

Large-scale integration of electric vehicles (EVs) into the city system for charging will affect the operation of both traffic and distribution networks. An electric vehicle navigation and cluster dispatch model is proposed for improving the overall charging efficiency of EVs on the transportation network and increasing the voltage level of the distribution network. First, a simplified model of vehicles and traffic road network is established, and a cell transmission model (CTM) is used to simulate the real traffic network. The traffic system takes into account charging EVs, discharging EVs, and other vehicles, and traffic congestion is considered. Then, a coupled model of the traffic–power system is built for the orderly charging of electric vehicles upon arrival at a charging station. The model considers the coupling of the two systems on a time scale, and the charging/discharging power at each charging station node is controlled. The validity of the model is verified in a coupled system of 357 cell traffic network and modified IEEE33 nodes. The results show that the proposed model can ensure good guarantee of the distribution network voltage reliability and reveal the scheduling process of the traffic network. The proposed model also provides a reference for planning of charging stations in the distribution network.

Effective traffic routing for urban transportation capacity and safety enhancement

Traffic routing is an essential service in most intelligent transportation systems (ITS), especially in urban traffic networks of large cities. However, this is a challenge issue as these traffic networks are complicated and traffic conditions change dynamically. Existing routing solutions from technology giants such as Google Maps, Waze are still insufficient in terms of accuracy (effectiveness) and efficiency (performance), especially applying in developing countries where traffic infrastructures have not yet matured enough, leading to degradation of transportation performance, safety while increasing traffic jam, economic loss, air and noise pollution. This work proposes an effective routing approach in large cities to avoid traffic congestion, enhancing transportation capacity and safety. Concretely, this work designed an appropriate urban traffic data organization scheme and devised a novel approach to searching solutions in an optimal routing algorithm, thus improve the routing performance significantly. Results from real-field experiments confirm the effectiveness and the efficiency of the proposed approaches which are prominent compared to the state-of-the-art solutions. The proposed approaches have been applied in a real urban traffic network of Ho Chi Minh City, the biggest city in Vietnam whose traffic is complicated. The experimental results and the practical deployed system reveal that the proposed mechanisms are ready to be applied in real-world applications.

A quasi-contagion process modeling and characteristic analysis for real-world urban traffic network congestion patterns

The congestion propagation pattern in urban traffic networks has long been viewed as a complex question. Inspired by the fact that virus spread patterns are similar to traffic congestion propagation in some ways, this paper proposes a congestion propagation model (SIS-CP) to describe the congestion propagation patterns in large-scale traffic networks with very few parameters. In this SIS-CP model, the attribute of the real traffic network in the actual operation process is considered, a method based on Hilbert–Huang transform (HHT-based) is proposed to determine traffic states of links, and the congestion propagation patterns are analyzed. The real-world data of ‘DiDi Chuxing GAIA Open Dataset Initiative of Chengdu city from March to June is used to calibrate and validate the proposed models. The results show that the basic and limit value of congestion links’ ratio do exist in real traffic networks; the relationship between congestion links’ ratio and its derivative is conformed to the parabolic function; the HHT-based method can determine traffic states of links accurately; and the SIS-CP model can describe the congestion propagation patterns correctly.

Analysis of drivers’ continuous delay time effect on the lattice hydrodynamic model with the on-ramp

In the real traffic, drivers often perceive traffic information with delay time, while presenting asynchronous reactions that form non-constant delay time. Meanwhile, on-ramps are very common in the real traffic network environment, especially on highway sections. Traffic flow merging from the on-ramps has a significant impact on the traffic stability of the main road. Given these facts, we develop an extended lattice hydrodynamic model with drivers’ continuous delay time and on-ramp. Based on the linear and nonlinear stability analysis, we derive the stability criterion and modified Korteweg–de Vries (mKdV) equation of the proposed lattice model; the kink-antikink soliton wave solution obtained by solving the above mKdV equation can be used to describe the propagation and evolution characteristics of density waves near the neutral stable curve. Results show that the ramp inflow ratio and delay time have a significant impact on the stability of traffic flow on the freeway main lane.

Parallelization of the B static traffic assignment algorithm

A widely used technique for predicting traffic flows in individual roads of a road traffic network is the user-equilibrium (UE) traffic assignment (TA). This technique assigns trips from origins to destinations in a road traffic network so that all trips use the cheapest path. The cost of the path, which consists of roads (edges), is the sum of the roads costs. These costs increase with increasing flow in these roads. In this paper, we describe the parallelization of the B algorithm – a relatively new TA algorithm with a fast convergence to a solution. Since the nature of the algorithm and the nature of its fast convergence complicate the parallelization itself, we considered and implemented three parallel variants and tested them on real road traffic networks to investigate their convergence, usability, and speed. The parallelization is intended for a shared memory parallel computing environment. The description of the parallelization along with the performed tests is the main contribution of this paper.

Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia. Investigation is also performed using a grid traffic network (GTN) to understand that the proposed scheme is effective in a traditional traffic network. Our proposed scheme is evaluated using two simulation tools, namely Matlab and Simulation of Urban Mobility (SUMO). Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30% in the simulations.

Surrogate-assisted cooperative signal optimization for large-scale traffic networks

Reasonable setting of traffic signals can be very helpful in alleviating congestion in urban traffic networks. Meta-heuristic optimization algorithms have proved themselves to be able to find high-quality signal timing plans. However, they generally suffer from performance deterioration when solving large-scale traffic signal optimization problems due to the huge search space and limited computational budget. Directing against this issue, this study proposes a surrogate-assisted cooperative signal optimization (SCSO) method. Different from existing methods that directly deal with the entire traffic network, SCSO first decomposes it into a set of tractable sub-networks, and then achieves signal setting by cooperatively optimizing these sub-networks with a surrogate-assisted optimizer. The decomposition operation significantly narrows the search space of the whole traffic network, and the surrogate-assisted optimizer greatly lowers the computational burden by reducing the number of expensive traffic simulations. By taking Newman fast algorithm, radial basis function and a modified estimation of distribution algorithm as decomposer, surrogate model and optimizer, respectively, this study develops a concrete SCSO algorithm. To evaluate its effectiveness and efficiency, a large-scale traffic network involving crossroads and T-junctions is generated based on a real traffic network. Comparison with several existing meta-heuristic algorithms specially designed for traffic signal optimization demonstrates the superiority of SCSO in reducing the average delay time of vehicles.

Spatiotemporal-Dependent Vehicle Routing Problem Considering Carbon Emissions

Aiming to improve the timeliness of logistics distribution and render the optimized route scheme effective under the real traffic network, we study the green vehicle routing problem with dynamic travel speed from both dimensions of time and space. A discrete formulation is proposed to calculate the travel time based on periods and arcs, which allows a vehicle to travel across an arc in multiple periods. Then, we establish a mixed-integer nonlinear programming model with minimum distribution costs including transportation costs, carbon emissions costs, and penalty costs on earliness and tardiness. A hybrid adaptive genetic algorithm with elite neighborhood search is developed to solve the problem. In the algorithm, a neighborhood search operator is employed to optimize elite individuals so that the algorithm can stimulate the intensification and avoid falling into a local optimum. Experimental instances are constructed based on benchmark instances of vehicle routing problem. The numerical results indicate that the proposed algorithm is rather effective in global convergence. Compared with the routing schemes in which travel speed merely varies with time periods or locations, the vehicle route optimized on spatiotemporal-varying speed outperforms them in terms of carbon emissions and timeliness. The research can provide a scientific and reasonable method for logistics enterprises to plan the vehicle schedule focusing on spatiotemporal-dependent speed of the road network.

HybridFlow: Achieving Load Balancing in Software-Defined WANs With Scalable Routing

The scalability issue hinders the deployment of Software-Defined Networking (SDN) in the Wide Area Networks (WANs). Existing solutions have two issues: (1) network performance relies on complicated controller synchronization, which increases the complexity of network control; (2) fine-grained flow processing enables flexible flow control at the cost of high processing load on the controllers and high flow table occupancy on switches. In this paper, we propose a scalable routing solution named HybridFlow, which achieves a good load balancing performance using a single controller with low control overhead (i.e., flow routing and rerouting overhead). HybridFlow mainly employs two techniques: hybrid routing and crucial flow rerouting. Hybrid routing enabled by commercial SDN switches gives us opportunities to reduce the processing load of the controller by routing flows with the hybrid OpenFlow/OSPF mode. Thus, the majority of flows can be routed by OSPF without involving the controller. Crucial flow rerouting realizes load balancing by dynamically identifying crucial flows based on a new metric called Variation Slope and rerouting these flows with the hybrid OpenFlow/OSPF mode. The simulation based on the real traffic traces and network typologies shows that compared with the optimal solution, HybridFlow can achieve 87% of the optimal load balancing performance by rerouting 36% less flows on average.

Robustness evaluation for real traffic network from complex network perspective

Robustness is a critical characteristic of ubiquitous real networked systems which are often very vulnerable to the random failures or targeted attacks. In this work, we aim to evaluate the robustness of a traffic network which is constructed from real trajectory data. First, we divide a city into grids which are considered as nodes and a link is established if there is at least one taxi dispatch order from one grid to another. Second, we propose two categories of attack strategies: node attacks (e.g. random node failure, and degree, in-degree, out-degree, closeness, clustering coefficient, PageRank-based targeted attacks) and link attacks (e.g. random link failure and link weight, link degree, link betweenness-based targeted attacks). Third, we suppose all elements (i.e. node or link) are simultaneously or iteratively removed, and evaluate the network robustness by employing the relative size of the giant components, average clustering coefficient and average shortest path length. The results show that the degree-based targeted attacks are the most destructive among all attacks, and no matter node or link, the betweenness-based attack is the most effective method to be beneficial to the target index. Moreover, most methods with recalculations take priority over the ones without recalculations.

Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree (XGBT), which is the best performing regression model, together in a single optimization framework. Comparison and results are generated by two experiments (one synthetic and one from real urban traffic network) and show that the new BGA-ML is much faster than the original GA algorithm and can reduce the total travel time by almost half when used under incident conditions.

A microsimulation based analysis of the price of anarchy in traffic routing: The enhanced Braess network case

In the scientific literature, the ratio between the total travel cost under a user equilibrium assignment and the total travel cost under a system optimum assignment is typically referred to as the Price of Anarchy (PoA), i.e., the level of inefficiency that can be eliminated by centralized control in routing. Recently, this concept has been attracting renovated attention due to the new opportunities offered by vehicles’ connectivity and automation. The new technologies could allow individual prescriptions centrally managed to achieve benefits in network performance. However, considering the infrastructure that such a system would need and the ethical implications it could have (related to privacy, equity, etc.), it is necessary to carefully quantify its actual benefits. Existing PoA related studies do not fully capture the essential realism of traffic dynamics and show contradictory results. Moreover, there are no studies that explore the relation of the PoA and driving behavior (e.g., reaction time, acceleration, deceleration, aggressiveness, etc.), which is very important for designing the algorithms of automated driving. In this light, the present paper investigates the PoA over a Braess-like network using the most detailed way to model traffic—the microscopic simulation with parameters defining the driving behavior. The magnitude of the PoA has been studied over a wide range of combinations of the model input parameters. Results show that the PoA can be much higher than that obtained in theoretical studies. In addition, results are used to reveal some PoA features of real traffic networks and propose further research directions.