Traffic Planning Research Articles

An OpenStreetMap derived building classification dataset for the United States

Building classification is crucial for population estimation, traffic planning, urban planning, and emergency response applications. Although essential, such data is often not readily available. To alleviate this problem, this work presents a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We developed a dataset of building types based on building footprints and the available OpenStreetMap information. The dataset is validated using authoritative ground truth data for select counties in the U.S., which shows a high precision for non-residential building classification and a high recall for residential buildings. In addition to the building classifications, this dataset includes detailed information on the OpenStreetMap data used in the classification process. A major result of this work is the resulting dataset of classifying 67,705,475 buildings. We hope that this data is of value to the scientific community, including urban and transportation planners.

Integrated Analysis of Rockfalls and Floods in the Jiului Gorge, Romania: Impacts on Road and Rail Traffic

This study examines the impact of rockfalls and floods on road and rail traffic in the Jiului Gorge, Romania, a critical transportation corridor. Using Sentinel-1 radar imagery processed through ESA SNAP and ArcGIS Pro, alongside traffic detection facilitated by YOLO models, we assessed susceptibility to both rockfalls and floods. The primary aim was to enhance public safety for traffic participants by providing accurate hazard mapping. Our study focuses on the area from Bumbești-Jiu to Petroșani, traversing the Southern Carpathians. The results demonstrate the utility of integrating remote sensing with machine learning to improve hazard management and inform more effective traffic planning. These findings contribute to safer, more resilient infrastructure in areas vulnerable to natural hazards.

TPTrans: Vessel Trajectory Prediction Model Based on Transformer Using AIS Data

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least 6×10−4 compared with the baseline model.

A hybrid model for missing traffic flow data imputation based on clustering and attention mechanism optimizing LSTM and AdaBoost

Reliable traffic flow data is not only crucial for traffic management and planning, but also the foundation for many intelligent applications. However, the phenomenon of missing traffic flow data often occurs, so we propose an imputation model for missing traffic flow data to overcome the randomness and instability bands of traffic flow. First, k-means clustering is used to classify road segments with traffic flow belonging to the same pattern into a group to utilize the spatial characteristics of roads fully. Then, the LSTM networks optimized with an attention mechanism are used as the base learner to extract the temporal dependence of the traffic flow. Finally, the AdaBoost algorithm is used to integrate all the LSTM-attention networks into a reinforced learner to impute the missing data. To validate the effectiveness of the proposed model, we use the PeMS dataset for validation, we impute the data with missing data rate from 10 to 60% under three missing modes, and we use multiple baseline models for comparison, which confirms that our proposed model improves the stability and accuracy of imputing the missing data of the traffic flow with different scenarios.

Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization, reduction of traffic congestion, and reduction in the incidence of traffic accidents. However, data pertaining to traffic flow are typically influenced by a multitude of factors, resulting in data that exhibit a considerable degree of nonlinearity and complexity. To address the issue of noise in raw traffic flow data, this study proposes a hybrid model that combines variational modal decomposition (VMD), a bidirectional long short-term memory network (BiLSTM), and a gated recurrent unit (GRU) for short-term traffic flow prediction. To validate the effectiveness of the model, an experimental validation was conducted based on traffic flow data from UK highways, and the performance of the model was compared with common benchmark models. The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error, coefficient of determination, and root-mean-square error compared to existing prediction techniques, thereby substantiating its efficacy in short-term traffic flow prediction.

Do the sparser access points have less impact on arterial traffic? A microscopic simulation-based study

A dynamic microscopic traffic flow simulation within a ring arterial context was developed to investigate the effects of access point spacing on urban arterial flow under a right-in-right-out access management system. The microscopic traffic flow model, centered on car-following and lane-changing behaviors, was established based on vehicle interactions. The car-following aspect encompasses free driving, car-following behavior, and deceleration and braking states while lane-changing considerations include decision-making and acceptable gap assessment. Experimental scenarios account for arterial traffic density, access traffic demand intensity, average access point spacing, and variation coefficient of access point spacing. The traffic flow and speeds within the ring arterial were evaluated across 5040 operational conditions (equating to 5880 simulation hours). The traffic flow trends and speed variations with density across different access spacing scenarios were analyzed. We made an intriguing discovery: the impact on arterial traffic flow increases with larger average access point spacing, challenging conventional traffic planning recommendations that advocate for greater spacing. Additionally, access traffic minimally affects the overall arterial flow when arterial traffic volume is low. By highlighting these critical insights, this study introduces novel considerations for designing and managing access points.

Refining Cellular Data for Accurate Trip Chain Identification: A Novel Approach for Urban Travel Analysis

Cellular data play a crucial role in supporting travel demand assessment and urban traffic planning because of their cost-effectiveness and extensive coverage. However, inherent inaccuracies, such as imprecise positioning, data duplication, and abnormal communication frequencies, hinder their ability to depict travelers’ trips accurately. In this paper, we present a novel approach to mitigate these issues by employing base station mapping to refine positioning data, eliminating duplicates and abnormal frequency records. This refinement enables more effective utilization of cellular data for trip chain identification. We address the ping-pong handover effect by employing a finite automaton machine and an approximate nearest neighbor searching method with carefully selected seeds to identify activities. Our method’s accuracy is validated through ground truth value analysis, focusing on permanent resident population estimation and metro passenger flow estimation. Furthermore, we demonstrate the practical effectiveness and value of our proposed method through a series of representative applications in a real-world case study in the city of Nanning, Guangxi Province, China.

Impact of Debris Removal Post-Wildfires on Pavement Fatigue and Rutting Lives: Case Studies of California’s Camp and Carr Fires

Between 2017 and 2018, California experienced a series of four devastating fires, including the Camp and Carr Fires, which ranked among the most destructive fires in U.S. history. During these fires, roads were critical in the evacuation, rescue operations, goods transportation, and access to critical services. Additionally, postfire, road infrastructure became crucial for removing hazardous and nonhazardous waste from fire-affected areas to major landfills and recycling facilities. Despite the significance of pavements in this process, previous studies have not quantitatively assessed the potential damage caused to pavements by the additional trucks used in debris removal operations. This research aimed to address this knowledge gap by collecting precise traffic data for the routes taken to waste management facilities, including data on the number of trips involved in debris transportation. The traffic information was then utilized to calculate changes in equivalent single axle loads and traffic index values for pavement design. Pavement structures were obtained from the available core database. Pavement simulation results showed that of the nine studied highways, only one exhibited a reduction in cracking life of about 2 years. However, Skyway, the main artery in the town of Paradise, demonstrated a significantly accelerated fatigue cracking failure by 14.3 years. A sensitivity analysis of fire intensity showed other highway sections that were structurally adequate could be affected by larger fires. The presented methodology could be used in traffic planning as part of debris management operations to avoid vulnerable pavement sections.

Analysis of the environmental factors contributing to road traffic accidents involving school-aged pedestrian children in urban Algerian settings

Despite efforts by the Algerian government and civil society to improve road safety, traffic accidents remain a significant issue, particularly affecting children and youth. Data from the National Delegation for Road Safety (NDSR) reveals that this demographic is especially vulnerable, with 371 child fatalities recorded during the 2020-2021 school year. This study evaluated road safety around twelve primary schools in Oued El Abtal district, Mascara province, and aimed to identify effective interventions through a participatory approach involving 1,074 students. An anonymous questionnaire was used to capture students' perceptions of road risks, and the data were analyzed using IBM SPSS Statistics V27. Chi-square and Fisher’s exact tests were applied to examine associations between gender and variables like travel mode, school accompaniment, student behavior, and crossing locations. The sample was balanced by gender, consisting of 50.9% boys and 49.1% girls. Walking was the predominant travel mode (75.2%; n=808). Key risk factors included inadequate infrastructure (3.2%), insufficient signage (11.7%), sidewalk obstructions (5.1%), dangerous driving behaviors (12.2%, with speeding at 29.5%), heavy traffic (16.2%), and poor urban planning (e.g., lack of speed bumps and low visibility). These findings highlight the urgent need for targeted interventions to improve the safety of school commutes. This research identifies critical risk factors in school travel, providing a foundation for developing strategies aimed at enhancing road safety for children in Algeria.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

MODELS FOR REAL-TIME TRAFFIC FLOW MANAGEABILITY AND DECISION-MAKING IN INTELLIGENT TRANSPORTATION SYSTEMS

This article explores models in Intelligent Transportation Systems for real-time traffic flow manageability, focusing on decision-making processes. It covers forecasting, planning, implementing, and controlling strategies to manage traffic flow and ease congestion. Traffic flow prediction models, like dynamic route guidance and traffic flow prediction, utilize historical data and real-time inputs for proactive decision-making. Traffic flow planning models, such as dynamic route guidance index and route efficiency factor, aid in route selection and signal timing optimization. In order to streamline the boundless complexity, the authors assume that it is effective to delineate the managerial capacity paradigm of intelligent transportation systems into the two separate scenarios of “stable and known situation” and “unstable and with large uncertainty situation”. The article proposes a hypothesis to improve the decision-making process in traffic flow. The distinction between these two situations is essential for the smooth running of the business and requires a thorough understanding of the traffic flow in real time, making decisions in intelligent transport systems in order to direct the traffic. The article focuses on data-driven decisions for smoother traffic flow.

The Use of Smart Sensors to Enhance Road Safety: An Experimental Study to Explore the Usability of Smart Traffic Light Systems with Emergency Vehicles

The evolving landscape of transportation systems is being reshaped by the integration of sensors and Internet of Things (IoT) technology, particularly in the context of traffic light systems. These innovations aim to transform traditional traffic lights into smart devices capable of being controlled by emergency vehicles (EVs) to ensure safe passage, thereby enhancing road safety, and reducing intersection accidents. However, there are challenges in implementing such technologies and gaining the trust of EV drivers navigating busy intersections. The traditional fixed-time traffic signal plans are contrasted with the dynamic adjustment made possible by smart sensors and applications, which collect real-time traffic data and adapt signal timings to changing conditions, reducing congestion, and enhancing traffic flow. However, security concerns arise, necessitating the strengthening of systems with advanced technologies to address potential security and safety issues. The technological advancements have a significant impact on the transportation industry. Smart sensors in streetlights play a crucial role in solving long-standing transportation issues, and machine learning is employed for data analysis and traffic forecasting, benefiting both regular and EV drivers. Further research is needed to advance technologies like IoT and smart sensors and to integrate GSM sim cards and GPS to support EV drivers in finding the quickest route to their final destinations. EVs are becoming more than vehicles, evolving into smart devices equipped with technical tools allow drivers to control traffic lights, ensuring a clear path and more safety.

Enhancing Traffic Management in Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach

Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.

A Study on the Key Factors for the Sustainable Development of Shared Mobility Based on TDM Theory: The Case Study from China

This study is based on an investigation of shared mobility in Chinese cities, which identifies the factors affecting the sustainable development of shared mobility based on the theoretical framework of TDM (travel demand management). Through a literature review and expert interviews, the FUZZY-DEMATEL-ISM-MICMAC integration model was used to screen 21 influencing factors from aspects that fit the research theme. Triangular fuzzy numbers are used to quantify the subjective scores of nine expert groups and weaken the subjective influence of expert scores. The logical relationships among DEMATEL technology-building factors and ISM technology-based factors are divided into levels. The MICMAC technique is used to divide the types of factors according to the driving power and dependency. The results show that (1) the influence factors of the “soft strategy” and “hard strategy” in the framework of TDM are determined. In the soft strategy, we should focus on “shared mobility education” (shared mobility education, shared mobility publicity and shared mobility “environment” information) and “community organization” (community organization and advocacy and organizational interaction). In the hard strategy, we should focus on “traffic planning and measures”, “dedicated lanes”, “parking facilities”, and “financial subsidies”. (2) The ISM recursive structure model is divided into five layers. Among them, shared mobility education, shared mobility operating technology, and organizational interaction are at the deep root level, which can continuously influence other factors in the long run. (3) In MICMAC, the number of related factors is large. When making decisions on these factors, managers should comprehensively consider the correlation of factors and adjust the use of factors from an overall perspective. This study can help managers identify the key factors affecting the sustainability of shared mobility and make targeted recommendations.

ВЛИЯНИЕ АДАПТИВНОГО УПРАВЛЕНИЯ СВЕТОФОРНЫМИ ОБЪЕКТАМИ НА ХАРАКТЕРИСТИКИ ДОРОЖНОГО ДВИЖЕНИЯ СМЕЖНЫХ ПЕРЕКРЕСТКОВ

The article is devoted to the study of the impact of adaptive algorithms for controlling traffic lights at one intersection on changes in traffic at adjacent intersections. The main purpose of the study is to identify the advantages and disadvantages of implementing such algorithms to assess the impact on adjacent intersections. Three algorithms have been proposed for the study: queue skipping, phase duration redistribution, and waiting time estimation. The study included traffic modeling, data collection on waiting times, queue lengths, and an assessment of changes in traffic flows. Data was collected using smart cameras, which allowed us to identify how changing traffic light work plans at one intersection affects the dynamics of traffic at neighboring intersections. Possible changes in traffic at adjacent intersections are proved, in particular, an increase in the number of waiting vehicles and the average waiting time at adjacent intersections, which indicates the negative impact of adaptive algorithms on adjacent intersections.

The Forecasting Model of the Impact of Shopping Centres in Urban Areas on the Generation of Traffic Demand

The increase in traffic caused by new development affects the change in traffic conditions on the surrounding roads, and shopping centres are significant traffic generators. The development of local travel generation rates and their characteristics for individual land uses from the aspect of traffic demand is a reliable way to plan traffic in order to come up with preventive solutions to traffic problems, that is, prevention of possible negative consequences on traffic conditions in the street network occurring due to the construction of shopping centres. One of the main aims of this paper is to develop a model for objective assessment of the generated traffic demand for significant changes in land use, such as the construction of shopping centres in medium-sized towns. All these would be steps in the right direction for the promotion of reliable traffic planning and adoption of TIA for every new development before a decision regarding the change in land purpose has been made. This kind of process still has not been established systematically in either Bosnia and Herzegovina and the Republic of Serbia, or in surrounding countries. This paper focuses on the formulation of a model for determining the volume of traffic generated by shopping centres in medium-sized towns in two countries of the Southeast Europe region. The survey was conducted in eight different locations (cities) where there are shopping centres with common facilities. The analysis showed that the number of visitors and vehicles attracted by the shopping centre zone can be determined by a model based on a linear regression analysis. The analysis included exploring several different factors of trip generation in shopping centres, including the relationship between trip generation and combinations of several independent variables. The verification of the model was conducted in real conditions of the traffic flow generated by a shopping centre which was not the analysis subject when forming the forecasting model. In this way, the validity of the proposed model is credibly assessed. The developed model can be applied in the procedures of planning the construction of shopping centres in medium-sized cities in the Republic of Serbia and Bosnia and Herzegovina, and wider, in the region of Southeast Europe, in order to estimate the volume of generated traffic demand, that is, its impact on the conditions of traffic on the surrounding traffic network.

Exploring the Relationship between Data Sample Size and Traffic Flow Prediction Accuracy

Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.

Flow prediction of mountain cities arterial road network for real-time regulation

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.

Research and Application of Traffic Flow Prediction in Chengdu City Based on Neural Network Algorithms

As urban traffic systems increase in complexity, accurately predicting traffic flow has become a critical task in traffic management and planning. This paper employs neural network technologies, specifically Graph Convolutional Networks (GCN) and Long Short-Term Memory networks (LSTM), to develop a traffic flow prediction model for Chengdu that incorporates spatio-temporal characteristics. Through empirical analysis, the model demonstrates high accuracy and good generalization capability, effectively predicting and analyzing traffic flow at different times and regions. Additionally, this study explores the application of the prediction model in traffic law regulation and policy formulation, providing data support and scientific basis for advancing smarter traffic systems.

Expressway Network Model Construction and Road Section Impedance Estimation Based on Mixed Data

The acquisition of high-speed road network information and traffic environment information is of great significance for traffic management and planning, and at the same time, an accurate and efficient road network model is also the basis for the acquisition and application of traffic parameters. In order to quickly obtain high-speed road network information and road section traffic state information, this paper takes Chongqing Highway as an example, analyzes the traffic condition and characteristics of the highway network, and establishes the topology of the high-speed road network using network data and actual high-speed toll data from the graph theory and complex network theory. At the same time, based on the topology theory, fully exploiting the high-speed toll data, a more accurate estimate of different time periods, vehicles in the high-speed road section of the travel time, for highway traffic analysis to provide data reference, to improve the traffic planning program to enhance the efficiency of traffic management, to promote traffic planning and decision-making, as well as to enrich the theoretical basis of transportation research is of great significance.