Traffic Management Planning Research Articles

Supervised machine learning for jamming transition in traffic flow with fluctuations in acceleration and braking

Jamming transition in traffic flow refers to the sudden transition from a free-flowing state to a jammed state as the traffic density increases. This transition is of great interest to traffic engineers and physicists, as it can have significant implications for traffic safety, efficiency, traffic management, and urban planning. Homogeneous car following models is a popular framework used to study the jamming transition phenomenon. The mathematical structure of the problem is governed by the classical Lorenz system to consider the fluctuational effects. The analytical solution of such nonlinear oscillatory differential equations does not exist. Therefore, this study aims to utilize the machine learning approach with the optimization technique that could be used to fine-tune the weights/ parameters of a neural network model to predict the accurate and reliable solutions for the jamming transition in traffic flow. The headway deviations have been studied by considering the multiple scenarios based on the acceleration and braking of the vehicle.

Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting

Traffic flow forecasting is a basic function of intelligent transportation systems, and the accuracy of prediction is of great significance for traffic management and urban planning. The main difficulty of traffic flow predictions is that there is complex underlying spatiotemporal dependence in traffic flow; thus, the existing spatiotemporal graph neural network (STGNN) models need to model both temporal dependence and spatial dependence. Graph neural networks (GNNs) are adopted to capture the spatial dependence in traffic flow, which can model the symmetric or asymmetric spatial relations between nodes in the traffic network. The transmission process of traffic features in GNNs is guided by the node-to-node relationship (e.g., adjacency or spatial distance) between nodes, ignoring the spatial dependence caused by local topological constraints in the road network. To further consider the influence of local topology on the spatial dependence of road networks, in this paper, we introduce Ollivier–Ricci curvature information between connected edges in the road network, which is based on optimal transport theory and makes comprehensive use of the neighborhood-to-neighborhood relationship to guide the transmission process of traffic features between nodes in STGNNs. Experiments on real-world traffic datasets show that the models with Ollivier–Ricci curvature information outperforms those based on only node-to-node relationships between nodes by ten percent on average in the RMSE metric. This study indicates that by utilizing complex topological features in road networks, spatial dependence can be captured more sufficiently, further improving the predictive ability of traffic forecasting models.

A Model for Identifying Road Risk Class

Abstract In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles. This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.

A multi-stage fusion network for transportation mode identification with varied scale representation of GPS trajectories

Accurate transportation mode identification is essential for traffic management and travel planning. The rapid development of GPS-enabled devices has made it both popular and cost-effective to obtain travel modes from massive GPS trajectory datasets. However, since different transportation modes exhibit significantly different spatial characteristics, varied scale representation can be used to efficiently capture these differences, but existing research have failed to fully exploit this point.To address these issues, we propose a novel deep learning framework named Multi-Attribute-Scale-Object-based Multi-Stage Fusion Network (MASO-MSF). A MASO structure is constructed to represent the local motion states and spatial characteristics of one GPS trajectory segment at different spatial scales. Subsequently, a multi-stage fusion model (MSF) is designed to perform accurate transportation mode identification in an end-to-end manner. In this model, an attribute channel fusion module is built to fuse local motion states and capture the spatial dependencies. A scale feature fusion module is then established to selectively aggregate features from different spatial scales. Finally, MSF deploys an object decision fusion module to generate the final identification result from different probabilities of multiple objects in a trajectory segment.To evaluate the performance of the proposed MASO-MSF model, a series of experiments are conducted on a publicly available dataset. The experimental results verify the effectiveness of MASO in characterizing a trajectory segment from three perspectives (i.e., attribute, scale, and object) and demonstrate that MASO-MSF can achieve the state-of-the-art identification performance. In addition, the source code of MASO-MSF is now available at GitHub (https://github.com/MYL23/MASO-MSF).

Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution

Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this article, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate dynamic graph, which is integrated with pre-defined static graph. As far as we know, we are first to employ a generation method to model fine topology of dynamic graph at each time step. Furthermore, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently. Source codes are available at https://github.com/tsinghua-fib-lab/Traffic-Benchmark .

Traffic Impact Assessment for Sustainable Development Along the Highways in Bangladesh: Considerations for a New Industrial Setup

A useful method for evaluating the effects of traffic generated by a proposed development on the local transportation system is the traffic impact assessment (TIA). A TIA typically contains a description of the size and complexity of the planned project, an overview of the anticipated effects, and any upgrades that may be necessary to make the roadway infrastructure safe for the proposed development. With this study's design horizon in mind, we hope to ensure that the transportation system will operate efficiently and safely. Development must have the necessary utilities and transportation systems to sustain itself. Every proposed development must then include an evaluation of the site's appropriateness in terms of factors like compatibility with highways and plans, access location geometry, and capacity and traffic control, among others. The main objective of the TIA report is to identify whether a particular development project will have an impact on the safety and efficiency of adjacent roads. Moreover, the report paves the way for additional study and the development of traffic management plans to reduce traffic congestion. TIA is also needed to support the EIA being prepared for any project development along the highway road. So, it is important to evaluate TIA while new industrial installations are set up along the Highways in Bangladesh.

Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines.

Aircraft scheduling optimization model for on-ramp of corridors-in-the-sky

<abstract> <p>To efficiently handle the projected growth in air traffic, operational concepts such as "corridors-in-the-sky" have been developed in future Air Traffic Management (ATM) plans. Corridors-in-the-sky are defined as reserved airspace where aircraft move in a common direction along parallel lanes on high flight levels. The on and off ramps play an important role in connecting this airspace structure to the jet route. This paper addresses the scheduling problem for flight sequences entering the corridors-in-the-sky via the on-ramp. To satisfy the requirements of various stakeholders, this paper proposes time-based and cost-based aircraft scheduling optimization models for the on-ramp of the corridors-in-the-sky. An improved Particle Swarm Optimization (PSO) algorithm is also developed to solve a case study of the air route B215 in the Beijing control area. Computational results indicate that the optimized models have advantages in reducing the delay time and cost for both single aircraft and flight sequences, compared with the First-come, First-served (FCFS) approach.</p> </abstract>

Investigating Travel Flow Differences between Peak Hours with Spatial Model with Endogenous Weight Matrix Using Automatic Vehicle Identification Data

The rapid urbanization has brought great challenges to the transportation network. However, travel flow at peak hours is not always the same. It is important to investigate how travel flow differs between peak hours to capture travel flow patterns and influential factors to facilitate traffic management and urban planning. This paper establishes a spatial model with endogenous weight matrix (SARBP-EWM) to investigate the travel flow differences between morning and evening peaks on both weekday and weekend based on automatic vehicle identification (AVI) data and point of interest (POI) data in Xuancheng, China. The results confirm strong spatial effects and endogeneity issue. Besides, facility variables such as number of offices and number of clinics reveal strong negative impacts on travel flow differences on both weekday and weekend, while the number of middle school shows significantly positive relation with travel flow differences. In addition, the endogenous weight matrix on both weekday and weekend is successfully estimated and compared. It is found that TAZ pairs tend to be clustered with lower spatial weights on weekday, while they are more randomly distributed with higher spatial weights at weekend. Based on the results above, the policies proposed from Xuancheng 14th Five-Year Plan are evaluated and discussed. The above empirical analysis quantifies impacts from key factors on urban travel flow differences between peak hours and provides important references for urban planning and policy making.

Copula ARMA-GARCH modelling of spatially and temporally correlated time series data for transportation planning use

Time series analysis has been used extensively in transport research in various areas, such as traffic management and transport planning. Time-series data may contain temporal and spatial correlations. Temporal correlation refers to the dependency of observations on time, while spatial correlation refers to the dependence of observations on space. Although there is a substantial amount of literature on statistical models that modelled temporal correlation of transport time-series data, there are few studies that accommodate both temporal and spatial correlations. In cases where more than one time-series dataset is collected from different locations, each observation can depend on its previous values and observations from other locations. Transport-related time-series data (e.g., traffic volumes, travel times, etc.) may exhibit nonlinear spatial correlation within the transport network. However, existing statistical time series models that consider spatial correlation, such as vector auto-regression (VAR) models, are limited to linear spatial correlation functions with a time-invariance assumption. This study introduces a copula time series model capable of modelling correlations between variables through time and space. Temporal correlations are modelled by an autoregressive moving average-generalised autoregressive conditional heteroskedasticity (ARMA-GARCH) model, where the conditional mean is predicted based on past values and errors, and conditional variance is predicted based on past residuals and conditional variances. This enables the model to describe heteroscedastic data. The spatial correlation is modelled with a copula model, which has the flexibility to model different types of correlations, such as nonlinear, tailed and asymmetric correlations. We illustrate the statistical properties of our approach where the performance of the copula ARMA-GARCH model is compared with the VAR model based on mean absolute percentage error (MAPE) and root mean square error (RMSE) values of model predictions on synthesised data. We then demonstrate practical implementations of these models examined in two case studies with traffic count data collected from four main arterial roads in Sydney.

Periodic Attention-based Stacked Sequence to Sequence framework for long-term travel time prediction

Travel time analysis and prediction are keystones for building intelligent transportation systems in the new era, which has gained wide attention from the research community. Over the last few years, deep learning-based approaches have demonstrated the capability to predict travel time with big data. However, the existing works focused on predicting short-term travel time for paths and roads. In fact, knowing a period of long-term future travel time is a demand with important applications like traffic management and schedule routing planning. Nevertheless, studies on long-term traffic prediction are still very limited due to the complicated underlying factors. In this paper, we propose a novel deep learning-based framework named Periodic Attention-based Stacked Sequence to Sequence (PASS2S), which aims to address the long-term traffic prediction problem. PASS2S consists of two main components, namely periodic segment generation and attention-based stacked sequential prediction. To extract periodic information, we design a periodic segment generation component to capture historical periodic segments from the traffic data. To reduce the error propagation and improve long-term prediction accuracy, we propose an attention-based stacked prediction component to model long short-term and short-term dependencies from the periodic segments. We conducted a series of experiments on a real-world travel time dataset and the experimental results show that our proposed approach outperforms the state-of-the-art competing methods in terms of various metrics like MAE, RMSE, and SMAPE. To the best of our knowledge, this is the first work that considers attentive periodic historical information for solving the long-term travel time prediction with a period of the future, which has not been well studied in the research community.

Multi-Dimensional Attention Based Spatial-Temporal Networks for Traffic Forecasting

Traffic flow prediction is the key problem of intelligent transportation system. Accurate prediction results are indispensable for traffic management and road planning. However, due to the complex spatial-temporal correlation of traffic flow data, including the spatial correlation and temporal correlation of adjacency, periodicity, and trend that exist between different roads. The existing forecasting methods consider the spatial-temporal correlation but lack the dynamic modeling of spatial-temporal correlation. To deal with this dynamic feature, this paper proposes a multi-dimensional attention-based spatial-temporal network (MA-STN). It mainly contains three parts, the spatial-temporal attention unit, the spatial-temporal feature extraction unit based on Graph Convolutional Network (GCN) and the fusion prediction unit, and the residual connection is also added to the model to avoid the gradient disappearance problem. Meanwhile, this paper divides the dataset into three subsets to deal with the three features in the temporal dimension separately. To verify the effectiveness of the proposed model, two real-world road traffic flow data collected by PeMS system are used for validation. By comparing six different models, the proposed network in this paper has a 7% accuracy improvement compared to the baseline model. To verify the effectiveness of the attention mechanism, ablation experiments are used in this paper for validation, and the results show that the attention mechanism can achieve a 5% accuracy improvement.

A Review on Work Zone Safety During Blackspot Improvement Construction Under Blackspot Management

Abstract: Massive Road building and development is being taken up in India, under various schemes, such as National Highway Development Project (NHDP) and Prime Minister’s Rural Road Development Schemes (PMGSY). Activities of improvement of about 45,000 kms of National Highways and around 400,000 kms of rural roads besides numerous urban roads are going on, all across the country. To ensure safety of all, there is a need to adopt an efficient and effective plan for management of traffic in work zones which are already blackspots. Work Zone Traffic Management Plans (WTMPs) are required to meet the safety needs of regular traffic as well as works traffic, ensuring minimum disruption in access to properties and movement of pedestrians. This research is a case study of road accident scenario on NH-152(Ambala-Zirakpur section). In 28.05 kilometers long stretch it has 24 identified accidental black spots, indicates that about 12-13 km of the road length has engineering faults and lack adequate infrastructure as per the requirements of the vehicles/pedestrians. This research talk about management of activities involved during its blackspot rectification work in work zone areas. As per this approach we have divided whole stretch into 5 corridors and described management of activities with main concern of safety, cost and time. At last, we have compared traditional approach of blackspot improvement and corridor approach of black spot improvement and finally concluded our study in form of guidelines which can be adopted during black spot rectification using corridor approach.

The Impact of Rainfall on Urban Human Mobility from Taxi GPS Data

Rainfall severely impacts human mobility in urban areas and creates significant challenges for traffic management and urban planning. There is an urgent need to understand the impact of rainfall on residents’ travels from multiple perspectives. Taxi GPS data contains a large amount of spatiotemporal information about human activities and mobility in urban areas. For this study, we selected the central area of Zhuhai as the study area and used taxi data from August 2020 for the investigation. Firstly, we divided the taxi data into four scenarios, i.e., weekdays with and without rainfall and weekends with and without rainfall and analyzed and compared the trip characteristics for the different scenarios. Then, using the traffic analysis zone (TAZ) as the node and taxi flow between TAZs as edges, we constructed a network and compared the network indicators under the different scenarios. Finally, we used the Leiden algorithm to detect communities in different scenarios and compared the network indicators of the communities. The results showed that on days with rainfall, taxi flow and its spatial and temporal distribution pattern changed significantly, which affected transportation supply and demand. These findings may provide useful references for the formulation of urban transport policies that can adapt to different weather conditions.

Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers’ clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles’ abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle’s actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations.

Detecting spatiotemporal traffic events using geosocial media data

Social media platforms enable efficient traffic event detection by allowing users to produce geo-tagged content (e.g., tweets) known as geosocial media data. Geosocial media data improve road safety by providing timely updates for traffic flow and traffic control. Recent studies on traffic event detection with geosocial media data have been focused around keyword-based query approaches, where the event content was inferred by predetermined categories, to retrieve relevant traffic events. Spatiotemporal features associated with traffic-related posts have not been fully investigated. In this study, we filtered irrelevant posts with association rules. A spatiotemporal clustering-based method was then used to retrieve traffic events from these filtered posts, where the content of detected events was automatically inferred with a set of representative terms. For comparison, a typical text classification-based method was also used by classifying the posts filtered from association rules into different categories. By validating the detection results with vehicle travel speed data, we demonstrate that the former outperforms the latter in terms of the number of correctly detected traffic events from one-year of Twitter data in Toronto, Canada. Our proposed approach helps organizations and governments to be aware of when and where traffic events occur by identifying event hotspots and peak periods, which improves both traffic management and urban planning.

Impact of the COVID-19 pandemic on urban human mobility - A multiscale geospatial network analysis using New York bike-sharing data

The COVID-19 pandemic breaking out at the end of 2019 has seriously impacted urban human mobility and poses great challenges for traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we propose a multiscale geospatial network framework for the analysis of bike-sharing data, aiming to provide a new perspective for the exploration of the pandemic impact on urban human mobility. More specifically, we organize the bike-sharing data into a network representation, and divide the network into a three-scale structure, ranging from the whole bike system at the macroscale, to the network community at the mesoscale and then to the bicycle station at the microscale. The spatiotemporal analysis of bike-sharing data at each scale is combined with visualization methods for an intuitive understanding of the patterns. We select New York City, one of the most seriously influenced city by the pandemic, as the study area, and used Citi Bike bike-sharing data from January to April in 2019 and 2020 in this area for the investigation. The analysis results show that with the development of the pandemic, the riding flow and its spatiotemporal distribution pattern changed significantly, which had a series of effects on the use and management of bikes in the city. These findings may provide useful references during the pandemic for various stakeholders, e.g., citizens for their travel planning, bike-sharing companies for bicycle dispatching and bicycle disinfection management, and governments for traffic management.

Construction Readiness for Highway Projects: Key Decision Criteria

The impacts of highway construction delays are not limited to the construction industry, but also affect the nation’s overall economic and social conditions. One factor that can cause delays is the project team rushing into construction without assessing the project’s readiness. In other words, adequately assessing the readiness of a project before construction may prevent delays. Therefore, this study aims to identify key decision criteria (KDC) to assess the construction readiness of highway projects. To achieve this objective, 109 surveys completed by industry practitioners were analyzed using the mean ranking technique, normalization method, agreement analysis, and factor analysis. A total of 18 KDC for assessing the construction readiness of highway projects were identified. In addition, the underlying categories for the KDC were reduced uncertainties, availability of resources, approvals and permits, and adequate traffic management plans. This study contributes to the current body of knowledge by identifying a list of KDC for the construction readiness of highway projects. This study’s practical contribution is the list of KDC that industry practitioners can use to assess the readiness of highway projects 1prior to construction.

Traffic Impact Assessment, Diversion and Management Plan for Surat Metro Line 1, Package –CS3, Surat

Abstract: Package CS3 of Metro Line 1 in Surat City includes 3 stations i.e. Surat railway station, Maskati Hospital and Chowk Bazar. As per proposed Metro plan of Surat city, this line will originate from Sarthana station and will terminate at Dream City. The length of Line 1 is 21.61KM of which 14.59km is elevated whereas 7.02km is Underground and consists of 20 Stations. This metro line envisages use of public transport system in Surat city and shall cater the present and future travel demand of the catchment area and shall also reduce load from road based transport system of the corridor. During the construction phase of any Mass Rapid Transit System (MRTS) running along the Right of Way (ROW) of existing roadway system, Traffic diversion and management plan implementation becomes absolute mandatory to reduce congestion, conflicts increase level of safety and ease construction process. Similarly, for package CS3 of Surat Metro line 1, there is need of preparing an implementing Traffic Diversion and Management plan to create a synergy amongst construction activities, traffic flow, safety of pedestrian and construction worker with minimal impact on surrounding catchment. This study shall provide Traffic diversion and management plan which may help to cater the existing traffic and stir them in a smooth and non-congested flow with the help of signage’s, road markings, etc.

Remote Sensing Image Vehicle Detection Based on Pre-Training and Random-Initialized Fusion Network

Vehicle detection based on remote sensing images is of great significance to intelligent traffic management and smart city planning. The performances of the mainstream vehicle detection methods such as Fast-RCNN, SSD, FCRN, etc. are mainly affected by their extracted features, whose richness and robustness can be enhanced by introducing pre-training models from other datasets. However, the weights of the pre-training models used in traditional methods like FCRN, only shallow-tuned from the weights of another database training, without deep optimization from random initialization. This letter proposes a new pre-training and random-initialized fusion network (PRFN): the random initialization model and the pre-training model are connected in parallel, and an encoder is used to fuse their features, thereby improving the richness of features and the robustness of the model. We evaluate four different combination patterns of the pre-training and random initialized models, and the effect of the fusion feature encoder. Experiments show the parallel concatenating pattern with the fusion feature encoder achieves the best performance than other patterns. We test on three datasets, our PRFN exceeds the traditional methods, and, respectively, 1.23%, 1.04%, 0.94% higher than the baseline. All codes are available from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LHKRobert/PRFN</uri> .