WiP: Traffic Flow Reconstruction from Scattered Data

A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model

This paper presents an adaptive traffic signal controller algorithm that allows real time traffic flow data to control traffic signal timing for an isolated intersection. The proposed design allows greater flexibility and control of signal timings while keeping in view multiple criterions. It is based on simple rules that allow traffic flows on all intersection branches to control the timing of current signal phase without causing unrealistic delays on low volume side streets. The algorithm is also adjusted for capacity of different links. Detector data has been used to assess congestion situation and respond accordingly. A priority scheme for priority vehicles is also proposed. As a proof of concept, the proposed algorithm has been implemented on FPGA as a parameterized VERILOG model allowing adjustment to any intersection size and topology. An evaluator circuit was designed to assess the performance as compared to actuated signal controller. Improvement was observed in total stopped delay, number of stops and queue lengths.

"Marine Traffic Simulation System" has been developed by the first author and others for long years. It has three simulation modes. First mode is normal navigation mode. It reproduces realistic traffic flows according to the predefined planed route. Second mode is collision avoidance mode. It has the feature of automatic collision avoidance for all ships besides the normal navigation mode. Third mode is external data mode. It reproduces traffic flow according to the data given by such as AIS. With collision avoidance mode, more realistic traffic flow can be reproduced. However, the validation of the simulation result is not yet done, because it was difficult to get the real traffic flow data before AIS data acquisition system established. This is one of the main purposes of this paper. The system is applied for southward area from Tokyo Bay. Assuming external mode as a real traffic flow, other two modes are evaluated. Normal navigation mode overestimates the navigational risk, while collision avoidance mode reproduces it fairly well in terms of several indices to represent the risk of the area. Three indices; number of occurrence of CR≧0.9, near-miss, and collision respectively, are proposed to assess the navigational risk and discussed. The normalization of these indices, traffic density and gaming area is also proposed and verified including the simulation results of two other areas.

Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data

Local data are essential for the analysis of abrupt conditions in traffic flow. In this research, we address the shortcomings of real-time traffic flow detection data, which inadequately represent the density characteristics of vehicular traffic. To mitigate this issue, we establish mathematical relationships among traffic flow density, time headway, and vehicle speed, based on the fundamental characteristics of traffic flow dynamics. This framework allows for the estimation of density across various road segments. Recognizing the nonlinear dynamics that characterize traffic flow, we introduce a microscopic traffic flow cusp catastrophe potential function that incorporates headway, utilizing cusp catastrophe theory alongside microscopic traffic flow parameters to analyze real-time variations in traffic flow potential characteristics. In the experimental section, we developed fitting regression functions for traffic flow parameters using road detection data from the cities of Nanjing and Yueyang in China. We also compared outcomes derived from the theoretical model with the parameter estimation results obtained from the data, which facilitated the calculation of traffic flow characteristics, such as cusp density and volume over a one-month period. The results derived from the proposed potential function exhibit the functions’ greater capacity to capture the complex fluctuations in traffic flow compared to traditional traffic potential functions, thereby providing a valuable instrument with which to evaluate traffic flow characteristics in conjunction with real-time monitoring data.

The study of traffic flow characteristics is essential for designing highway systems for which different traffic influencing variables are of primary importance. The study aims to assess the impact of a range of traffic flow and density levels on the spatio-temporal (lateral placement and time headway) traffic characteristics of highways. A substantial amount of traffic data like speed, time headway (TH), and lateral placement (LP) of vehicles was collected continuously for 12 h on six Indian highway sections. The DPCU for different vehicles with different flow and density rates were calculated. The speed and TH descriptive statistics and probability distribution functions at varying flow and density levels were also analyzed. To determine the most appropriate probability distribution function, the goodness of fit test was used. Furthermore, the impact of traffic speed, flow, and density on the LP of vehicles under different traffic conditions was assessed using descriptive statistics. It was observed that the traffic flow and density levels had an unusual effect on the DPCUs of vehicles. The mean traffic speed under high flow and density decreased by 33.3 and 18.3%, while the mean TH under high flow and density decreased by 28.4 and 29.9%. Also, the results indicated that traffic speed and TH data exhibit a different distribution of probability functions depending on the traffic flow and density. The descriptive statistics on the LP of vehicles show the existence of a significant difference of the same concerning the different ranges of traffic speed, flow, and density.KeywordsSpeedFlowDensityTime headwayLateral placementMixed traffic

Urban road congestion is increasingly serious, in order to optimize the traffic situation, and improve the efficiency of vehicles, so as to provide service for traveling user. Then analysis four major systems, namely the bus electronic station board system, evaluation system of urban road traffic state, urban traffic guidance system and website demonstration system. Regarding average travel speed, congestion coefficient and mean velocity gradient as evaluation index of evaluation system, to simulate the bus electronic station board to obtain corresponding real-time traffic flow data. A website design platform is used for dynamic demonstration of urban road traffic state, and the evaluation result is determined. Finally, the valid data will be released to the scene of traffic for reasonable traffic grooming.

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

Traffic incidents continue to cause a significant loss in deaths, injuries, and property damages. Reported traffic accident data contains a considerable amount of human errors, hindering the studies on traffic accidents. Several approaches have been developed to detect accidents using traffic data in real time. However, those approaches do not consider the spatiotemporal patterns inherent in traffic data, resulting in high false alarm rates. In this paper, we study the problem of traffic accident detection by considering multiple traffic speed time series collected from road network sensors. To capture the spatiotemporal impact of traffic accidents to upstream locations, we adopt Impact Interval Grouping (IIG), which compares real-time traffic speed with historical data, and generates impact intervals to determine the presence of accidents. Furthermore, we take a multivariate time series classification approach and extract three novel features to measure the severity of traffic accidents. We use real-world traffic speed and accident datasets in our empirical evaluation, and our solutions outperform state-of-the-art approaches in multivariate time series classification.

In contemporary traffic flow theory, the distinction between user classes is rarely made. However, we envisage that the accuracy and the descriptive power of the macroscopic traffic flow models can be improved significantly by separating user classes and their specific flow characteristics. As a consequence, the possibility of improved estimation and prediction of traffic flow conditions becomes available. Additionally, the availability of a realistic multiple user-class traffic flow model enables the automated generation of user-dedicated traffic control policies by means of mathematical optimal control theory. A macroscopic multiple user-class model is derived from mesoscopic principles. In opposition to earlier multiple user-class models, the model presented here implicitly defines equilibrium relationships between traffic density and equilibrium velocities as a function of current traffic conditions, traffic composition, and distribution of user-class-dependent desired velocities. Additionally, the velocity variance variable is introduced describing deviations from the average speed within the user classes. The multiple user-class model identifies competing processes. On the one hand, drivers attempt to traverse the freeway at their desired velocity; on the other hand, they adjust their velocity because of interaction with slower vehicles. These processes can result in self-formation of apparently random local structures. Finally, the proposed model satisfies the anisotropy condition and the invariant personality condition.

Road traffic emission is considered to be the major source of pollution exposure in megacities around the globe. Traffic emission makes urban air pollution to be highly spatially heterogeneous with sharp concentration gradients that can vary substantially within a few meters near the road. The spatially heterogeneous and temporally varying emissions are required to account for  concentration gradients that have a direct impact on the population exposure to outdoor air pollution. However, estimating such a detailed emission is very complex as it requires multi-category emission factors and a huge amount of georeferenced detailed traffic activity data such as traffic volume and speed, distance traveled, vehicle category share, fuel share, engine share, technology share etc. In the absence of detailed data, emission estimations have been limited to coarser resolution which may not be suitable for high resolution air quality modeling, exposure assessment and management..Here we present an emission model to estimate multi-pollutant hourly gridded on-road traffic emission over Delhi. The model uses the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. For traffic activity, the emission model uses advanced traffic volume and speed data for Delhi obtained from TRIPP (Transportation Research and Injury Prevention Programme, IIT Delhi). Further the model considers the congestion (travel time delay based on TOMTOM) and speed-volume relation for different road categories to estimate hourly traffic volume and speed for each road link in Delhi that is used to calculate the hourly emissions using the COPERT emission functions Further, the emissions are gridded at 100 m × 100 m resolution to generate high-resolution spatio-temporal emission maps for Delhi and shown in Fig. 1 for four different hours of the day.We analyzed the modeled emissions to identify peak emission hours, pollution hotspots and most polluting vehicles. The hourly variation of emissions show distinct bimodal distribution with morning and dominant evening peaks for almost all pollutants linked with congestion and peak traffic.Figure 1. Estimated gridded NOx emission at 100m × 100m spatial resolution at different time of the day; the time is displayed in the upper-right corner of each subplot.The emissions are high near the busy roads and traffic junctions. The emission flux in the central areas of Delhi (Fig. 1) is 40-50% higher than mean emission flux due to the higher road and traffic density and lower average speed. Diesel vehicles have been found to be the dominant contributor to PM, BC and NOx emission. Our results suggest that the top 5 polluting vehicle categories account for more than half (55% - 91%) of the emissions. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. The developed model can be applied for developing emission inventory and real-time emission with the growing availability of real-time traffic data.

<p>This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.</p>

Vehicle speed plays an important role in the traffic flow and road density. This parameter is often researched to obtain a model of the characteristics of an existing road section. This study aims to analyze vehicle speed on arterial roads using the Greenshield Model approach, which is a fundamental traffic model that describes the relationship between traffic density and vehicle speed. Through the collection of field data and statistical analysis, vehicle speed patterns and factors influencing them can be identified. The method used is t distribution analysis with the Greenshield Model approach to see the comparison of vehicle speed values on the Kenjeran highway and Kertajaya Indah road sections. The results show that the Greenshield Model can be effective in predicting traffic speeds on arterial roads, providing insights for better transportation planning and road design. Furthermore, the mathematical equation that can be produced on arterial roads is with an average speed value of 26.9 km/h and a standard error of 0.504 and an R2 correlation of 0.658. The conclusions of this study offer guidance for urban planners and transportation engineers in optimizing traffic flow and improving road user safety y=32.282-0.0347x.

With the traffic stream data collected in the exit ramp downstream of south Ruijin road located in the Shanghai inner loop expressway, traffic parameters of traffic speed, density, occupancy and volume are analyzed. The time variation of speed and volume, the difference of traffic volume participating rate in the various lanes of road section downstream, the selection and evaluation of density-speed model and the relationship among traffic density, speed and volume are key-points analyzed. The research can provide data support and scientific basis for the design and planning of urban expressway.

Vehicles offer people many conveniences, but at the same time cause considerable problems, for example for the environment. Today, the electrification of the automobile is considered the best way to solve at least a large part of the environmental problems caused by motor vehicles. Apart from the fact that an electric powertrain produces no local emissions, it also differs from a conventional fossil-fuel powertrain in its propulsion dynamics. It is therefore also to be expected that electric vehicles will influence the flow of traffic due to their different dynamic properties. This thesis focuses on using microscopic traffic simulation to provide a first estimation of how the electrification of vehicles could change the traffic flow and to what extent different traffic situations would influence the fuel and energy consumption of vehicles. In most microscopic traffic flow models, drivers and vehicles are modeled together by a combined model in one simulation object. By adjusting some parameter values, different combinations of driver and vehicle can be roughly distinguished. The values are determined on the basis of observational data of the traffic flow. However, the currently still small share of electric vehicles in road traffic and the fact that they look little different from combustion engine driven and hybrid vehicles make it difficult to collect data from electric vehicles in traffic flow, unless the data is collected in fleet tests. In this thesis, drivers and vehicles were modeled separately. The focus of this thesis is on the assessment of the influence of vehicle propulsion on traffic flow. Therefore, the model developed in this thesis was based on the physical capabilities of both the fossil-fuel powered and the electric powered vehicles. The driver model, which was also required, was created on the basis of data of human drivers, which were determined in an accompanying investigation of a driving simulator. Driver and vehicle model were combined again in the microscopic traffic simulation to a car-following model. For observing the impact of electric vehicles on traffic flow and estimating energy consumption of vehicles, a traffic scenario of a part of the City of Duisburg was established. The effect of electric vehicles on traffic flow, as well as the effect of traffic density on fuel and energy consumption was studied based on this scenario. The simulation results indicate that electric vehicles could raise the average speed of traffic flow to a certain degree, when traffic load and driving styles of the drivers remain the same. The increase of the share of electric vehicles has a positive effect on traffic efficiency. In situations with higher traffic density, energy consumption of electric vehicles is far superior to that of vehicles with internal combustion engine powertrains.