Real-time route optimization in smart cities via Bidirectional A* algorithm

Recent rapid development of intelligent transportation systems (ITS) makes it possible to improve the efficiency and reliability of transportation networks. Several articles have been presented regarding this topic in the Special Issue of Journal of Advanced Transportation. Wang and Khattak indicated that there is spatial heterogeneity in information acquisition and user decisions. Bekhor and colleagues showed that cellular phone technologies could be used to provide travel data for development of national travel demand model. Based on the deployment of automatic vehicle identification (AVI) technology in urban road network in Beijing, China, Feng and co-researchers proposed a particle filter method for vehicle trajectory reconstruction with the use of AVI and traffic count data. Yang and co-researchers proposed two proactive VSL control models for use on recurrently congested freeway segments. The embedded traffic flow relationships are adopted in the first proactive model to predict the evolution of congestion pattern and to optimize the speed limit on freeway segments with VSL. Using multi-source sensor data, Lu and researchers proposed a Kalman filter model for estimation of time-dependent origin?destination (OD) matrix that is a key input to the self-adaptive traffic control systems for real-time traffic management.

Link travel speeds in road networks are essential data for a variety of research problems in logistics, transportation, and traffic management. Real-world link travel speeds are stochastic, and highly dependent on speeds in previous time periods and neighboring road links. To understand how link travel speeds vary over space and time, we uncover their distributions, their space- and/or time-dependent correlations, as well as partial correlations, based on link travel speed datasets from an urban road network and a freeway network. We find that more than 90% (57%) of travel speeds are normally distributed in the urban road (freeway) network, and that correlations generally decrease with increased distance in time and space. We also investigate if and how different types of road links affect marginal distributions and correlations. The results show that different road link types produce quite similar marginal distributions and correlations. Finally, we study marginal distributions and correlations in a freeway network. Except that the marginal distribution and time correlation are different from the urban road network, others are similar.

A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index

An adaptive deep multi-task learning approach for citywide travel time collaborative estimation

Big data traffic forecasting is considered as one of the most important traffic management techniques on urban road networks. Big data in intelligent transportation system refers to the large amount of travel information. Various forecast schemes have been proposed to manage the traffic big data. Travel big data has been collected by videos, sensors, and mobile phone services. Videos, sensors and cellular networks are not sufficient for collecting data because of their limited coverage and expensive costs for installation and maintenance. To overcome the limitation of mentioned tools we introduce the GNSS application. Application of GNSS in travel time is proven to be efficient in terms of accuracy. GNSS big data will be managed to reduce traffic congestions and road accidents. This paper introduces a short-time forecast model based on real–time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting the travel speeds using EMA Model. Furthermore it is a significant requirement to introduce suitable control strategy for longitude based on GNSS Application. The GNSS products provide worldwide and real-time services using precise timing information, positioning technologies.

The assessment of the risk associated with urban road networks, particularly in the context of earthquakes, is of paramount importance for the identification and reinforcement of the most vulnerable sections of urban road networks and the selection of optimal emergency rescue routes. This paper proposes an innovative method combining Bayesian networks and multifactor decision theory. It considers the effect of uncertainty brought about by earthquakes on the composition of road networks and distinguishes critical sections that make up road networks under the influence of multiple factors. This enables suggestions to be made for postearthquake emergency rescue work. Seismic hazards can cause structural damage to urban road networks and affect normal access. This paper quantifies the risk of earthquakes to urban road networks and evaluates the seismic capacity of the network by estimating the travel time of each road section and the connectivity of road sections. A Bayesian network model is established, with the pre‐earthquake connectivity of each partial road section defined as the priori probability. The data of the Bayesian network is updated based on the information obtained from the observation of the components and the system. Multiattribute decision theory is employed to calculate the a posteriori probability, which is then related to the travel time and the length of the road sections, as well as the critical parts of the road network and the optimal emergency paths. This paper presents a case study in which the effectiveness of the decision analysis model is verified. The results of this study contribute to the improvement of emergency rescue operations following an earthquake and reinforce critical sections of the road network in advance, thereby enhancing the overall seismic resilience of the road network.

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

Urban road networks are lifelines for cities in fulfilling the transportation needs of their inhabitants. The Patna Urban Agglomeration Area (PUAA) lacks properly planned roads; many of them have varying widths, with encroachments that reduce effective road width. A serviceability analysis is required through a traffic survey in order to create a traffic flow profile. This profile aids in performing time-based path, elevation, and serviceability analyses. In this study, traffic data were collected using cameras at vital road junctions and signals. A manual traffic survey was conducted at locations where active traffic was observed during peak hours. The road network of the study area was created using Google Maps, digitizing roads as lines and utilities as points. The traffic survey data, the road network, and the utilities were analyzed in the Network Analyst tool of the ArcGIS software. The analyses revealed suitable routing at underpass and overpass, as well as feasible paths during peak hours and locations with poor utility access. The analysis focused on the low-income group of people who depend on public transport and utilities and are the driving force of a developing economy. Suitable solutions are suggested to improve the existing road network.

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales—namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

A Multi-Scale Analysis of 27,000 Urban Street Networks

In this paper we present an algorithm for processing aggregate nearest neighbor queries in time-dependent road networks, i.e., given a road network where the travel time over an edge is time-dependent, a set of query points Q, a set of points of interest (POIs) P and an aggregate function (e.g., sum), we find the k POIs that minimize the aggregated travel time from the query points. For instance, considering a city's road network at a given departure time and a group of friends at different locations wishing to meet at a restaurant, the time-dependent aggregate nearest neighbor query, considering the sum function, would return the restaurant that minimizes the sum of all travel times to it. The main contribution of our work is the consideration of the time-dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing aggregate nearest neighbor queries. Our approach is based on the ANNQPLB algorithm proposed by Htoo et al. and uses Hub Labels, proposed by Abraham et al., to compute optimistic travel times efficiently. In order to compare our proposal we extended the previously proposed ANNQPLB algorithm aimed at non-time dependent aggregate nearest neighbor queries, enabling it to deal with the time-dependency. Our experiments using a real road network have shown our proposed solution to be up to 94% faster than the temporally extended previous solution.

The urban road network shares self-similarity and is suitable to adopt the fractal theory for analysis. The fractal dimension of the urban road network reflects the completeness of the road system. The employment of fractal theory to analyze the urban road network can reflect the feasibility of the urban road network layout and is of vital significance to the evaluation of the urban road network planning. GIS has strong space analysis function and can directly show the analysis process. To use the fractal theory to analyze the urban road network on the GIS platform can provide a new idea for the evaluation of the urban road network. Based on the GIS platform, this paper employs the fractal theory to the analysis of Shenzhen's municipal road network. Hausdorff method is adopted to calculate the index of the road network coverage, analyze the calculation results and put forward suggestions to optimize the road network.

Theoretical research is conducted on finding the shortest path with stochastic and time-dependent characteristics of link travel time in urban road networks. Considering the influence of signalized intersections on travel time, the research first presents a function of waiting time at signalized intersections and analyzes its characteristics. Then, considering the reliability of travel time, the travel time model under the min-max theorem is established, and a mathematical proof that the stochastic time-dependent traffic networks can be reduced to a deterministic time-dependent network is presented by using the first mathematical induction. Finally, based on analyzing the characteristics of the shortest path and minimum travel time, which vary with the start time, we propose solving the shortest path problem with a shortest path algorithm based on Dijkstra’s algorithm that takes the waiting time at signalized intersections into consideration. The research results showed that the algorithm proposed in this study does not depend on the acquisition of the probability distribution of travel time compared with the traditional algorithm. The range of uncertain travel time can be derived from historical data and travelers’ experiences, and the obtained shortest path has more robust time reliability.

The reasonable connection between the external freeway network and the urban road network can alleviate the traffic pressure in the city. The fusion evaluation between the external freeway network and the urban road network are studied. Firstly, considering from three aspects of the facilities and the service performance and the connection point operation quality, the evaluation indicator system of two networks is established. Then, based on the cloud model and fuzzy comprehensive evaluation method, the fusion evaluation method based on cloud model - fuzzy comprehensive evaluation method of two networks is established, and the fusion evaluation process is determined. Finally, the effectiveness and scientific of the fusion evaluation method are verified by a case analysis of the skeleton road network and the external freeway of Changsha city.

Personalized travel time estimation for urban road networks: A tensor-based context-aware approach