Traffic Planning Research Articles

Çoklu terk ediş noktası bulunan pistler için stokastik sıralama planlama modeli

The runway exit points (REPs) of the airport are constructed considering the operational performance of different types of aircraft based on historical flight data. In sequence planning, it is assumed that aircraft will vacate the runway from an expected exit point. However, real performance can be uncertain, and the same type of aircraft may vacate the runway from different exit points rather than the expected point. In addition, the runway occupancy times (ROTs) of aircraft that vacate the runway from the same exit point may not be equal. This situation brings two types of uncertainty when making traffic plans in an airport with several REPs. The first uncertainty is the REP of the aircraft, and the second is the ROT uncertainty considering the exit points. In this study, a two-stage stochastic programming model was developed for aircraft sequencing in an airport that has multiple runway exit points. In the model, both runway exit and ROT uncertainties are considered. A runway with multiple exit points at an airport in Turkey was selected and flight track data of 154 arrival flights to this runway was examined. Various expected time of arrival and departure (ETAD) scenarios were generated based on real data and integrated into the mathematical models. The proposed model was then compared with deterministic and first come first serve (FCFS) approaches in terms of total delay. As a result of the comparison and analyses, the presented stochastic programming model provided robust solutions and delay savings compared to the other approaches.

Map-Matching Error Identification in the Absence of Ground Truth

Map-matching of trajectory data has widespread applications in vehicle tracking, traffic flow analysis, route planning, and intelligent transportation systems. Map-matching algorithms snap a set of trajectory points observed by a satellite navigation system to the most likely route segments of a map. However, due to the unavoidable errors in the recorded trajectory points and the incomplete map data, map-matching algorithms may match points to incorrect segments, leading to map-matching errors. Identification of these map-matching errors in the absence of ground truth can only be achieved by visual inspection and reasoning. Thus, the identification of map-matching errors without ground truth is a time-consuming and mundane task. Although research has focused on improving map-matching algorithms, to our knowledge no attempts have been made to automatically classify and identify the residual map-matching errors. In this work, we propose the first method to automatically identify map-matching errors in the absence of ground truth, i.e., only using the recorded trajectory points and the map-matched route. We have evaluated our method on a public dataset and observed an average accuracy of 91% in automatically identifying map-matching errors, thus helping analysts to significantly reduce manual effort for map-matching quality assurance.

Data-Driven Approach to Assess Street Safety: Large-Scale Analysis of the Microscopic Design

Safety is an important quality of street space that affects people’s psychological state and behavior in many ways. Previous large-scale assessment of street safety focuses more on social and physical factors and has less correlation with spatial design, especially the microscopic design. Limited by data and methods, street safety assessment related to microscopic design is mostly conducted on the small scale. Based on multisource big data, this study conducts a data-driven approach to assess the safety of street microscope design on a large scale from the perspective of individual perception. An assessment system including four dimensions of walkability, spatial enclosure, visual permeability, and vitality is constructed, which reflects the individual perceptions of the street space. Intraclass correlation coefficient (ICC) and location-based service (LBS) data are used to verify the effectiveness of the assessment method. The results show that multisource big data can effectively measure the physical elements and design features of streets, reflecting street users’ perception of vision, function, architecture, and street form, as well as the spatial selectivity based on their judgment of safety. The measurement of multidimensional connotations and the fusion of multiple data mining technologies promote the accuracy and effectiveness of the assessment method. Street safety presents the spatial distribution of high-value aggregation and low-value dispersion. Street safety is relatively low in areas with a large scale, lack of street interface, large amount of transit traffic, and high-density vegetation cover. The proposed method and the obtained results can be a reference for humanized street design and sustainable urban traffic planning and management.

Predicting Traffic Performance During a Wildfire Using Machine Learning

Many places around the world periodically suffer from wildfires that threaten lives and disrupt normal traffic operations. Poor traffic performance during wildfires can inhibit the effectiveness of evacuations. Understanding traffic performance during a wildfire would therefore help transportation operators develop emergency traffic control plans. In this study, we developed a traffic speed and flow prediction model that uses support vector regression (SVR), for use during wildfire incidents. This was constructed using historical data for wildfires in California from 2010 to 2019, which were paired with records of the traffic speed and flow on adjacent highways and the prevailing weather conditions during the wildfire events. The results showed that traffic performance during a wildfire could be predicted using the SVR model. Based on our prediction results, we recommend that policies be implemented to encourage or mandate more detailed data collection of wildfire events, such as the fire’s boundary over time, to facilitate better prediction results in models like the one proposed in this paper. This paper should inspire further work on the topic to improve the model and provide a reliable prediction tool for transportation operators in the future.

Perceived Service Quality Attributes in Public Transport: Inferences from Complaints and Negative Critical Incidents

A sample of 200 complaints filed to a public transport company and 210 negative critical incidents (NCIs) obtained from on-board interviews were analyzed with the purpose of inferring perceived service quality (PSQ) attributes of public transport. The most frequent complaints and NCIs concerned employee behavior and punctuality, followed by missing or inaccurate information and inadequate planning. In a follow-up mail survey, a representative sample of 997 respondents reported if they remembered having experienced prototypical NCIs constructed on the basis of the complaints. Confirming the validity of the inferred PSQ attributes, all NCIs were reported to have been experienced by at least some respondents. NCIs related to employee behavior were, however, less frequently remembered, whereas those related to vehicle design and space, punctuality, and traffic planning were more frequently remembered. Taken together, the results suggest that PSQ attributes in public transport refer to employee behavior, reliability, and simplicity. Finally, inferences made from customer complaints and negative critical incidents are shown to extend our knowledge of perceived service quality attributes in public transport.

Connected automated vehicle trajectory optimization along signalized arterial: A decentralized approach under mixed traffic environment

Trajectory optimization, as a key connected automated vehicles (CAVs) operation task, has the potential to mitigate traffic congestion, lower energy consumption, and increase the efficiency of traffic operation. This study proposes a decentralized approach to optimization CAV trajectories in both longitudinal and lateral dimensions along a signalized arterial under the mixed traffic environment, where human vehicles (HVs) and CAVs co-exist. More specifically, a 2-stage model is developed to optimize CAV trajectories based on traffic signal plans of downstream intersections and trajectory information of surrounding vehicles. The stage-1 is formulated to provide a rough estimate of the minimal travel time required for a single CAV traveling along this arterial with minimum stops. The stage-2 model is then designed to optimize the longitudinal and lateral behavior of CAVs with the objective of minimizing delay and lane-changing costs. This model is solved by a dynamic programming algorithm to satisfy the real-time optimization needs. A rolling horizon approach is adapted to dynamically implement the proposed model in light of changing traffic conditions. Numerical experiments have been conducted on a real-world arterial to evaluate the model performances. By comparing the optimized trajectories to the no optimization benchmark, the proposed model can reduce average stop delays of CAVs. Moreover, it can also reduce the stop delays of HVs and mixed traffic.

Analysis of the Factors Affecting the Construction of Subway Stations in Residential Areas

To design a more suitable scheme under different conditions so that subway stations can play their role better, this study investigated the construction elements of subway stations in residential areas. Metro stations in residential areas are generally located at the intersection of urban roads. As the “handover space” of the city, the construction principle should be based on people’s experience. As the core basis for traffic planning and future operation management, construction of subways in residential areas should take into consideration factors such as trip volume, distribution, and mode selection of residents, and be determined based on such mathematical models as the unit factor method, gravity model, and disaggregated analysis method. Station site selection is based on the point, line, and surface elements, and the importance of a station in the line network is judged by degree and betweenness centrality; the accessibility of the line network is determined by connectivity. In this study, the influencing factors of residential area subway station construction are divided into construction elements and site selection. Internal construction elements of stations include station entrances and exits, escalators, ticket machines, and transfer routes, and the conclusion of the mathematical model is used to select or give opinions about the internal construction elements of the subway. The point, line, and surface elements and the connection relationship between the subway and buses are used to determine the site selection of the subway. Furthermore, this paper discusses the three elements that affect the construction of the subway, comprehensively considers the functional requirements of the subway, and makes reasonable adjustments to each element. Finally, the requirements for the elements of the subway construction are determined.

Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan

Motor vehicle emissions are the primary air pollution source in cities worldwide. Changes in traffic flow in a city can drastically change overall levels of air pollution. The level of air pollution may vary significantly in some street segments compared to others, and a small number of stationary ambient air pollution monitors may not capture this variation. This study aimed to evaluate air pollution before and during a new traffic plan established in March 2019 in the city of Kandy, Sri Lanka, using smart sensor technology. Street level air pollution data (PM2.5 and NO2 ) was acquired using a mobile air quality sensor unit before and during the implementation of the new traffic plan. The sensor unit was mounted on a police traffic motorcycle that travelled through the city four times per day. Air pollution in selected road segments was compared before and during the new traffic plan, and the trends at different times of the day were compared using data from a stationary smart sensor. Both PM2.5 and NO2 levels were well above the World Health Organization (WHO) 24-hour guidelines during the monitoring period, regardless of the traffic plan period. Most of the road segments had comparatively higher air pollution levels during compared to before the new traffic plan. For any given time (morning, midday, afternoon, evening), day of the week, and period (before or during the new traffic plan), the highest PM2.5 and NO2 concentrations were observed at the road segment from Girls High School to Kandy Railway Station. The mobile air pollution monitoring data provided evidence that the mean concentration of PM2.5 during the new traffic plan (116.7 µg m-3) was significantly higher than before the new traffic plan (92.3 µg m-3) (p < 0.007). Increasing spatial coverage can provide much better information on human exposure to air pollutants, which is essential to control traffic related air pollution. Before implementing a new traffic plan, careful planning and improvement of road network infrastructure could reduce air pollution in urban areas.

Real-Time Adaptive Traffic Signal Control in a Connected and Automated Vehicle Environment: Optimisation of Signal Planning with Reinforcement Learning under Vehicle Speed Guidance.

Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions.

Deep Learning Enabled Fine-Grained Path Planning for Connected Vehicular Networks

In this paper, to alleviate the ever-increasing traffic congestion in urban areas by accommodating higher road traffic, we develop traffic prediction framework together with path planning method for connected vehicular networks. First, through the employment of convolutional neural network (CNN) and residual unit (RN), deep learning (DL) based fine-grained traffic prediction algorithm is designed to obtain the spatial-temporal characteristics of vehicular traffic. The regionally fine-grained traffic prediction framework can realize real-time traffic prediction of future changing trends at each road with a high accuracy and reliability. Second, we propose a gridded path planning method by making use of the traffic prediction information. The accuracy of selected path, complexity of path calculation, and adaptive path adjustment are jointly taken into consideration by achieving the refined traffic regulation in different gridded section. Finally, we utilize the actual vehicle data from the city of Beijing and digital map on OpenStreetMap to validate the effectiveness and reliability of the proposed traffic prediction framework and path planning method. Simulation results demonstrate that the proposed approach is capable of relieving urban traffic congestion based on the existing roadway systems, which can provide methodological guidance for data-intensive traffic management.

Computation and Optimization of Traffic Network Topologies Using Eclipse SUMO

The advent of modern computational tools in field of transportation can help to forecast the optimized vehicular routes and traffic network topology, using traffic conditions from real world data as inputs. In this study, the topologies of one-way and two-way street networks are analysed using microscopic traffic simulations implemented on the SUMO (Simulation of Urban MObility) platform were performed to analyse the effect of street conversion in Downtown Brickfields, Kuala Lumpur. It was found that one-way streets perform better at the onset of traffic congestion due to their higher capacity, but on average, the four-fold longer travel times make it harder to clear traffic by getting vehicles to their destinations than two-way streets. As time progresses, one-way streets' congestion may become doubly worse than that of two-way streets. This study may contribute to a more holistic assessment of traffic circulation plans designed for smart and liveable cities.

Features of intercity bus passenger group mobility behaviors in the context of smart tourism

The features of intercity bus passenger group mobility behaviors have important guiding significance for the transportation department. Based on passengers’ intercity bus ticket reservation records (roundtrips from Shanghai or Chongqing city) from a smart tourism app, the travel behaviors of these two groups of bus passengers are analyzed and compared. In each group, the passengers’ travelling interval time presents a power-law with a cutoff index, and the passengers’ travelling behaviors have negative memory and low burstiness. Also, travel distance displays a scale-free property, and it is more likely to have an exponential distribution. Furthermore, the difference in cyclotron radius between these two groups’ travelling distances is quite significant; roundtrips from Shanghai are frequent. Last, holidays have a significant influence on passengers’ travel behaviors, which leads to more trips. The research conclusions are helpful to deeply understand the features of human mobility behaviors in theory, and can assist the transportation department in traffic planning in the application.

Методика оценки целесообразности назначения мультимодальной пассажирской перевозки

Purpose: To study the impact of multimodal transportations on long-distance passenger transportations at the landfill of “FPC” Ltd. since in modern realities, train traffic planning without taking into account passenger demand can reduce railway competitive advantages in the market of transport mixed kind transportation and in the future, can lead to passenger dissatisfaction. To show the importance of making traffic schedule in accordance with passenger demand. To consider the matter on necessity of the supplement of existing approaches to multimodal route selection and on possibility to apply a regression model to forecast passenger flows. To pursue the testing of the developed methodology on the example of particular multimodal route. Methods: Comparison of existing approaches to multimodal transportation organization and their complement. Methods of mathematical statistics for passenger traffic flow forecasting. Results: The influence of multimodal transportations on long-distance passenger transportations at “FPC” Ltd landfill is considered. The criteria of multimodal transportation are defined. The methodology for feasibility assessment of multimodal route organization is proposed that allows estimating expected profit from transportation organization as well as predicting transportation impact on transportation process indicators. Practical importance: The recommendations and approaches proposed in the developed by the author methodology can be used for multimodal route development. The developed algorithm makes it possible to assess the prospects for passenger traffic change and allows yet at the preliminary selection stage for multimodal transportation organization options to perform a preliminary analysis of this organization economic feasibility.

Pedaling through a virtually redesigned city: Evaluation of traffic planning and urban design factors influencing bicycle traffic

To achieve the 1.5-degree target of the Paris Climate Agreement, it is of great importance to promote environmentally friendly means of transport. In urban areas, shifting motorized trips to active transport modes (i.e., walking and cycling) is essential. Therefore, knowledge of walking and cycling is indispensable for planning and policy, which is the basis for targeted promotion of active forms of mobility. This paper aims to identify factors that specifically influence cycling and to derive recommendations for action for planning and policy in a virtual testbed. Specifically, the influence of traffic planning measures (i.e., structural infrastructure facilities for stationary and moving traffic as well as traffic regulations) and urban design measures (i.e., the design of public space) on the promotion of cycling will be shown. For this purpose, a virtual reality simulation was used, independent of different external conditions during field surveys and the necessity of time-, cost- and regulation-intensive structural changes. Using an improved bicycle simulator, 93 people cycled through 20 variations of an approximately 680 m long road section, surveying the effect of selected traffic planning and urban design parameters. Special attention was paid to the subjective safety of the cyclists and the attractiveness of the urban environment. Furthermore, three types of infrastructure were differentiated: No bicycle infrastructure (riding on the roadway), a bicycle lane, and a structurally separated cycle path next to the sidewalk. The virtual road section represented a real location. In addition to the findings gained from the physiological optimization of the simulator, the results show that roadside greenery had the highest effectiveness in terms of subjective safety and attractiveness. Other factors with a high influence were a speed reduction from 50 to 30 km/h when riding on the roadway with cars and the red coloring of the bicycle lane, each increasing the perception of safety. In contrast, a lack of a boundary line between the cycle path and the sidewalk was unsettling for those who rarely or never ride bicycles in their daily lives. In addition, restaurant and recreation areas increased the attractiveness of the road section, although entailing a lower perception of safety. Other factors, such as motor vehicle traffic volume or vehicles parked at the roadside, showed no significant effects on the evaluation of cycling.With the help of these findings, the consequences of traffic planning and urban design measures can be better assessed in subsequent virtual testbeds. Also, initial trends can be identified as to which planning instruments should be used as a priority to promote cycling in urban areas. Future work should validate the transfer of these insights into physical urban spaces to support further the mid-term transformation towards sustainable urban mobility using scientific evidence.

The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach

Introduction: Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies. Method: This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance. Results: This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area. Conclusion: These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces. Practical Applications: The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.

A novel extended Kalman filter-based optimized routing approach for IoV environment

The development of intelligent transportation system has divulged towards traffic management, planning and control that requires precise neighboring vehicle location anticipation for information transmission. The crucial problem is to maintain QoS parameters in high speed and varying vehicular topology environment. The paper presents a novel EK-PGRP (Extended Kalman filter- Predictive Geographic Routing Protocol) routing approach to anticipate neighbor location and to select the propitious neighbor for advancing packets from source to destination vehicle using extended Kalman filter for real-time V2V communication in both urban and highway vehicular environment. This is acquired according to spatial and temporal movement attributes; every vehicle has an anticipation model to anticipate its own and neighboring vehicle mobility. Moreover, if LMP (Local Maximum Problem) state is encountered, i.e., where a vehicle is unable to locate any neighbor nearer to destination than itself to forward an information packet; then it uses predictive prediction algorithm to overcome that state. The precision, robustness and coherence attributes of the proposed routing approach are illustrated via extensive simulations. EK-PGRP is contrasted with K-PGRP (Kalman filter- Predictive Geographic Routing Protocol), PGRP (Predictive Geographic Routing Protocol) and GPSR (Greedy Perimeter Stateless Routing) routing protocols and results demonstrate that EK-PGRP outperformed most of the simulation cases and attained the minimum location error while ameliorating prediction accuracy of the vehicles in vehicular environment. The simulations were performed on MATLAB R2018a along with traffic simulator SUMO.

Fortifying Smart Transportation Security Through Public Blockchain

Smart vehicles-enabled intelligent transportation system (ITS) supports a wide range of applications, such as, but not limited to, traffic planning and management, collision avoidance alert system, automated road speed enforcement, electronic toll collection, and real-time parking management, to name a few. However, it suffers from various types of security and privacy issues due to insecure communication among the entities over public channels. Therefore, an efficient and lightweight security mechanism is essential to protect the data that is both at rest as well as in transit. To this direction, we propose a public blockchain-envisioned secure communication framework for ITS (PBSCF-ITS). The proposed PBSCF-ITS guarantees access control and key management among the vehicle to vehicle, vehicle to roadside unit, and roadside unit to cloud server. We analyze the security of PBSCF-ITS to prove its resilience against various types of possible attacks. Furthermore, the performance of PBSCF-ITS with other related competing schemes has been compared. The obtained results illustrate that PBSCF-ITS outperforms the existing ones. Additionally, the pragmatic study of PBSCF-ITS is conducted to check its influence on various network-related performance parameters, like the number of mined blocks and transactions per block.

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.

Satellite Fog Detection at Dawn and Dusk Based on the Deep Learning Algorithm under Terrain-Restriction

Fog generally forms at dawn and dusk, which exerts serious impacts on public traffic and human health. Terrain strongly affects fog formation, which provides a useful clue for fog detection from satellite observation. With the aid of the advanced Himawari-8 imager data (H8/AHI), this study develops a deep learning algorithm for fog detection at dawn and dusk under terrain-restriction and enhanced channel domain attention mechanism (DDF-Net). The DDF-Net is based on the traditional U-Net model, with the digital elevation model (DEM) data acting as the auxiliary information to separate fog from the low stratus. Furthermore, the squeeze-and-excitation networks (SE-Net) is integrated to optimize the information extraction for eliminating the influence of solar zenith angles (SZA) on the spectral characteristics over a large region. Results show acceptable accuracy of the DDF-Net. The overall probability of detection (POD) is 84.0% at dawn and 83.7% at dusk. In addition, the terrain-restriction strategy improves the results at the edges of foggy regions and reduces the false alarm rate (FAR) for low stratus. The accuracy is expected to be improved when training at a season or month scale, rather than at a longer temporal scale. Results of our study help to improve the accuracy of fog detection, which could further support the relevant traffic planning or healthy travel.

A Data Fusion Powered Bi-Directional Long Short Term Memory Model for Predicting Multi-Lane Short Term Traffic Flow

In intelligent transportation system (ITS), accurate and real-time prediction of short-term multi-lane traffic flow with existing traffic data is an important part of urban traffic planning, traffic management and control. The data generated in the process of vehicle driving has the cooperative characteristics of multi-source, space-time and dynamic. Combining the data with high-performance computing or cloud computing, a new space-time information framework is designed, which is of great significance for the analysis and prediction of traffic flow, as well as intelligent traffic management, service and decision-making. This paper analyzes the statistical characteristics of urban road traffic flow from the two dimensions of time and space through the spatial-temporal correlation between multi-lane short term traffic flow in single observation point and multi observation points. We constructed a data fusion powered bi-directional long short term memory (DFBD-LSTM) model for individual lane and aggregate traffic flow, then used this model to predict multi-lane short term traffic flow. By taking individual lane traffic flow and aggregate traffic flow as different variables, the model produces more accurate predictions, which can better guide people to travel, alleviate the congestion of urban road traffic network to a certain extent, and improve the utilization rate and transportation efficiency of traffic road.