Road networks are typically connected at the national scale, thus investigating a city's road network vulnerability requires defining an urban boundary. However, the choice of boundary varies widely ...in research, resulting in divergent and at times contradictory findings. Moreover, analyzing larger cities' road networks with expanding boundaries helps to predict road network vulnerability patterns in smaller, similarly structured cities. Guided by these two objectives, this study investigates the gaps in road network vulnerabilities by comparing 1452 road networks from 363 cities in Mainland China across four distinct boundary types. The findings indicate a significant change in road network vulnerability across different boundaries, with the largest gap ranging between 0.5449 and 0.8743. The study further finds these vulnerability gaps correlate with the difference in the structural characteristics of those urban road networks and the urban population covered by different boundaries, with correlation coefficients ranging from 0.362 to 0.755. Notably, the cities' vulnerability rankings show relatively slight variations when adopting different boundaries, with Spearman's correlation coefficients between 0.442 and 0.879. This research offers valuable insights into the influence of urban expansion on road network vulnerability, particularly relevant in the context of rapid urbanization and the expansion of road networks.
•Analyze 1452 road networks across 363 cities via four boundary types in China•Boundary choices significantly alter vulnerability assessments in Chinese cities.•City vulnerability rankings show relatively minor shifts across different boundaries.
In late 2015 three of the co-authors of this paper published the first review on time-dependent routing problems. Since then, there have been several important algorithmic developments in the field. ...These include travel time prediction methods, real-time re-optimization by operating directly on the road graph, efficient exploration of solution neighborhoods, dynamic discretization discovery and Machine Learning-inspired methods. The aim of this survey is to present such research lines, together with indications on their further developments.
•We review recent algorithmic developments in Time-dependent Vehicle Routing.•These include travel time prediction methods and real-time re-optimization.•We also cover methods based on dynamic discretization discovery.•Finally, recent Machine Learning-inspired methods are considered.•Indications on their further developments are presented and discussed.
•The role of road network patterns in zonal pedestrian safety was investigated.•A global integration index was used to quantify the topological structures of road networks.•Spatial CAR models were ...developed with three different proximity structures.
Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong.
The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.
Rapid urbanization and increasing demand for transportation burdens urban road infrastructures. The interplay of number of vehicles and available road capacity on their routes determines the level of ...congestion. Although approaches to modify demand and capacity exist, the possible limits of congestion alleviation by only modifying route choices have not been systematically studied. Here we couple the road networks of five diverse cities with the travel demand profiles in the morning peak hour obtained from billions of mobile phone traces to comprehensively analyse urban traffic. We present that a dimensionless ratio of the road supply to the travel demand explains the percentage of time lost in congestion. Finally, we examine congestion relief under a centralized routing scheme with varying levels of awareness of social good and quantify the benefits to show that moderate levels are enough to achieve significant collective travel time savings.
Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been the focus of many studies in the last few decades. Existing traffic flow prediction models mainly ...extract static spatial-temporal correlations, although these correlations are known to be dynamic in traffic networks. Attention-based models have emerged in recent years, mostly in the field of natural language processing, and have resulted in major progresses in terms of both accuracy and interpretability. This inspires us to introduce the application of attentions for traffic flow prediction. In this study, a deep learning based traffic flow predictor with spatial and temporal attentions (STANN) is proposed. The spatial and temporal attentions are used to exploit the spatial dependencies between road segments and temporal dependencies between time steps respectively. Experiment results with a real-world traffic dataset demonstrate the superior performance of the proposed model. The results also show that the utilization of multiple data resolutions could help improve prediction accuracy. Furthermore, the proposed model is demonstrated to have potential for improving the understanding of spatial-temporal correlations in a traffic network.
•A rainstorm process design method considering local rainfall patterns.•Impacts of rainfall pattern on vulnerability of road network are discussed.•The coupled model has satisfactory ...applicability.•Modes II and III are the most dangerous for the road network.
Waterlogging events in urban areas are becoming increasingly more frequent, which has led to tremendous economic losses. Urban road networks have also suffered heavy interference and destruction due to this type of disaster, and the precise assessment of road network vulnerability is an effective measure by which to reduce losses. Therefore, in this study, an analytical framework for the assessment of the vulnerability of road networks to urban waterlogging was constructed by using a coupled hydrodynamic model. The rainfall patterns in the study area were detected, and their impacts on vulnerability are discussed. The results show that rainfall events with unimodal, bimodal, and uniform shape patterns respectively account for 68.08%, 31.46%, and 0.47% of the total number of events considered in this study. The coupled hydrodynamic model used in this study is found to have satisfactory applicability for waterlogging simulation. Rainfall with a unimodal shape is found to have the greatest impact on the vulnerability of road networks, while that with a uniform shape has the least. Among the unimodal shapes, Mode II (late peak) and Mode III (middle peak) are the most dangerous.
In this modern era, traffic congestion has become a major source of severe negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic ...congestion is through future traffic prediction. The research field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Recently, researchers have started to focus on machine learning models because of their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its sheer prediction power which can be attributed to the complex and deep structure. Despite the popularity of deep neural network models in the field of traffic prediction, literature surveys of such methods are rare. In this work, we present an up-to-date survey of deep neural network for traffic prediction. We will provide a detailed explanation of popular deep neural network architectures commonly used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.
A number of traffic crash databases at present contain the precise positions and dates of these events. This feature allows for detailed spatiotemporal analysis of traffic crash patterns.
We applied ...a clustering method for identification of traffic crash hotspots to the rural parts of primary roads in the Czech road network (3,933 km) where 55,296 traffic crashes occurred over 2010 – 2018. The data were analyzed using a 3-year time window which moved forward with a one-day step as an elementary temporal resolution. The spatiotemporal behavior of hotspots could therefore be analyzed in great detail.
All the identified hotspots, during the monitored nine-year period, covered between 6.8% and 8.2% of the entire road network length in question. The percentage of traffic crashes within the hotspots remained stable over time at approximately 50%. Three elementary types of hotspots were identified when analyzing spatiotemporal crash patterns: hotspot emergence, stability and disappearance. Only 100 hotspots were stable (remained in approximately the same position) over the entire nine-year period. This approach can be applied to any traffic-crash time series when the precise positions and date of crashes are available.
•We studied spatiotemporal behavior of hotspots identified using the KDE+.•Crash data were analyzed using a 3-year time window with a one-day step.•All the hotspots covered between 6.8% and 8.2% of the road network.•Hotspots evolved over time, emerged or disappeared.•Only 100 hotspots were stable over nine-year period (2010–2018).
•The absence of functional hierarchy road network, together with the non-uniform layout of signals and accesses tends to deteriorate arterial safety.•A new modeling strategy was proposed to analyze ...the safety impacts of roadway network features (i.e., road network patterns, signal density and access density) on suburban arterials by applying a macro level safety modeling technique.•Bayesian Conditional Autoregressive models were developed for arterials covering 173 Traffic Analysis Zones in the suburban area in Shanghai.•The road network pattern with collector roads parallel to arterials was shown to be associated with fewer crashes than those without parallel collectors.•Lower road network density, higher signal density and access density tended to increase the crash occurrence on suburban arterials.
With rapid changes in land use development along suburban arterials in Shanghai, there is a corresponding increase in traffic demand on these arterials. To accommodate the local traffic needs of high accessibility and efficiency, an increased number of signalized intersections and accesses have been installed. However, the absence of a defined hierarchical road network, together with irregular signal spacing and access density, tends to deteriorate arterial safety. Previous studies on arterial safety were generally based on a single type of road entity, either intersection or roadway segment, and they analyzed the safety contributing factors (e.g. signal density and access density) on only that type of road entity, while these suburban arterial characteristics could significantly influence the safety performance of both intersections and roadway segments. Macro-level safety modeling was usually applied to investigate the relationships between zonal crash frequencies and demographics, road network features, and traffic characteristics, but the previous researchers did not consider the specific arterial characteristics of signal density and access density. In this study, a new modeling strategy was proposed to analyze the safety impacts of zonal roadway network features (i.e., road network patterns and road network density) along with the suburban arterial characteristics of signal density and access density. Bayesian Conditional Autoregressive Poisson Log-normal models were developed for suburban arterials in 173 traffic analysis zones in the suburban area of Shanghai. Results identified that the grid pattern road network with collector roads parallel to arterials was associated with fewer crashes than networks without parallel collectors. On the other hand, lower road network density, higher signal density and higher access density tended to increase the crash occurrence on suburban arterials.
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