Average Traffic Speed Research Articles

Modelling of Air Pollution Caused by Traffic Flows in Manado City, Indonesia

This study was aimed to determine the level of CO concentration due to traffic flows, know the traffic (vehicle) volume, traffic (vehicle) speed and wind speed, find out the relationship between traffic (vehicle) volume, traffic (vehicle) speed, wind speed and CO concentration using a regression model as well as examine the parameters influencing air pollution due to traffic flows. The primary data used in this study were the sample data of CO tested directly in the field and the sampling was done using Ecoline 6000 Gas Analyzer tool. The research sites covered 1) the segment of Sam Ratulangi Street in Manado representing the street locations with many multi-rise buildings and high building density, 2) the segment of Ahmad Yani Street in Manado representing the street locations with many trees, and 3) the segment of Pierre Tendean Street in Manado representing the street locations with open areas (beachsides). In this modeling, the independent variables were the total traffic volume, the average traffic speed as well as the wind speed and direction. The dependent variable was Carbon Monoxide (CO) with increased concentrations. Using the three independent variables, there were total 7 (seven) variable combinations used. Then, the obtained model was validated using the surveyed data. The maximum vehicle volume was 4,281.60 pcu/hour (pcu = passenger car unit) and the maximum vehicle speed was 32 km/hour. Meanwhile, the maximum wind speed generated was 7.5 km/hour and the maximum level of air pollution (CO) was 12.86 ppm (ppm = part per million). In this study, it was obtained the best model for each of the three locations. The results showed that the air pollution (CO) level of street locations with low wind speed, such as Sam Ratulangi street which is a closed area with many multi-rise buildings and high building density, was much higher than that of street locations with many trees growing in the median of streets with a distance of 1 meter from the edge of street pavement and also higher than that of street locations with open areas (beachsides). This is because a higher wind speed can disseminate or divide the concentration level of air pollution (CO) to various places. Air pollution control covers three stages namely the prevention, countermeasure, and recovery of air quality.

Cyclists do better. Analyzing urban cycling operating speeds and accessibility

Given the growing interest in promoting more sustainable urban transport, over the past years, many researchers have analyzed cycling mobility from different perspectives. However, some important aspects remain underexplored. One of them is the study of cycling speeds. The first goal of this research is to analyze the impact of a wide range of factor on cyclists’ speeds. Based on the examination of thousands of GPS routes, the investigation conducts diverse OLS regressions in order to analyze cyclists’ speed according to the diverse local factors that affect cyclists along their journey, such as the slope, the existence – and type – of bike infrastructures, the average traffic speed or the density of traffic lights or intersections. Cycling speed is also analyzed according to cyclists’ gender or age, the purpose of the journey, or even the weather conditions. The research includes the analysis of regular cyclists’ trips, as well as the analysis of bike-messengers’ routes. The results obtained shed light on the influence of these factors on cyclists’ speed by quantifying their specific impact, and diverse models predict cyclists’ travel times in the current scenario but also in future ones that may correspond to the implementation of new infrastructure or policies. In addition, the models allowed us to pursue the second goal of this study: to conduct a comparative analysis of accessibility for different transport modes, and then evaluate the competitiveness between them. The results evidence that cycling is not only a sustainable transport mode, but the most competitive for small-medium distances.

The Impact of a Surcharge on Taxi and For Hire Vehicle Trips on Traffic Congestion in New York City: Early Evidence from Traffic Sensors

As the number of automobiles continues to rise globally, traffic management has become increasingly challenging. A deep body of economic and social literature from Marshall (1920) and Weber (1892) onwards, suggests that humans and cities benefit from proximity. Proximity, comes at the cost, however, of congestion that by slowing movement countervails the agglomerative benefits proximity provides (Alonso, 1964, Mills 1992). Today, traffic in many urban centers has reached crisis levels (Barry, 2012), leading many cities to consider different forms of congestion pricing. On February 2, 2019 New York City began levying a $2.50 per trip tax on medallion taxis and $2.75 tax on ride-share vehicles traveling in much of Manhattan with the dual goals of reducing congestion and raising money for the Metropolitan Transportation Agency which funds mass transit (Herman, 2019). Further taxes are planned (Walker, 2019). To assess the tax’s initial effects on congestion, I study traffic data from the New York City Department of Transportation that is available through New York City’s Open Data initiative. Utilizing a variety of sensors situated around New York City, DOT tracks speeds electronically. For my research, I compare traffic speeds after the toll was levied with speeds beforehand. I find that in its first month, the surcharge levied on cars has not led to a decline in congestion operationalized as increase in average traffic speed. In fact, traffic speeds in Manhattan declined slightly in February suggesting greater congestion. More research is needed to assess if these findings are real or spurious, how the surcharge affects other variables such as real estate prices, and the applicability of these findings to other metropolitan regions.

The effect of electrified mobility on the relationship between traffic conditions and energy consumption

Decreasing road transport's harmful effects on environment and health and reducing road accidents are major policy priorities. A variety of technologies could drastically improve air quality, reduce energy consumption and CO2 emissions of road vehicles: in this respect, a prominent trend leverages Electric Vehicles (EVs), supported by improved performance and energy efficiency through connectivity and automation. A noteworthy research question in the transition from Internal Combustion Engine Vehicles (ICEVs) to the alternative technologies, is to understand how Intelligent Transport Systems and other traffic-related measures can contribute to the reduction of fuel consumption and greenhouse gas emissions. In fact, a widely acknowledged tenet assumes that congestion removal or mitigation in presence of ICEVs implies also a reduction of transport-related externalities. This paper explores whether this effect still holds for EVs, by performing an analysis of energy consumption over different vehicle trajectories, under both congested and free-flow conditions. Calculations are carried out using two vehicle simulators: the VT-CPEM (Virginia Tech Comprehensive Power-based Energy consumption model) model for EVs and the CO2MPAS (CO2 model for Passenger and commercial vehicle Simulation) vehicle simulator for the ICEVs, for both electric and conventional cases passengers and freight/commercial powertrains have been analysed. Results are presented on real and simulated data related to four powertrain-vehicle combinations, in terms of general trends of energy/fuel consumption versus speed. Interestingly, results show that, differently from ICEVs, the relationship between congestion and energy consumption underlying EVs can change with higher energy consumption connected to an increased average traffic speed.

Operational Considerations of Passing Zones for Two-lane Highways: Spanish Case Study

The U.S. Highway Capacity Manual (HCM 2010) methodology is used in Spain to evaluate traffic operation and quality of service. In two-lane undivided highways, the effect of limiting where drivers could pass slower vehicles, or passing restrictions, is considered through the percentage of no-passing zones. This measure does not account for how passing opportunities are distributed along the road. The objective of this research was to evaluate the effect percentage of no-passing zones and average passing zone length on a two-lane highway and, if significant, incorporate them in the analysis methodology,. The TWOPAS microsimulation program was calibrated and validated to the Spanish conditions. Passing restrictions had little effect on average traffic speed (ATS), with differences lower than 6 km/h between a road segment with no passing restrictions and a road segment with a passing restriction on 100% of its length. Conversely, passing restrictions can increase the percent time spent following (PTSF) up to 30%. Increasing the passing zone length beyond 2,000 m does not improve PTSF. The new models could be used to better estimate traffic operation on Spanish two-lane highways.

EXPLORATION ON QUANTIFYING CARBON DIOXIDE (CO2) EMISSION FROM ROAD TRAFFIC IN MEGACITY

Abstract. As the increasing concern for climate change, quantification on greenhouse gas (GHG) emissions in urban scale has been a key component for local climate actions. To explore an approach on estimating the GHG emissions closely to the real-world condition, in this paper, we make efforts on counting the carbon dioxide (CO2) emissions from road traffic in Tokyo. The road traffic emissions mapping is achieved by linking spatial road line data with detailed activity data (traffic census). Through the data processing by Geographic Information System technique, the emissions of each road segment are estimated basing on the daily average traffic amount and speed of vehicles on each road segment. As our estimation, the CO2 emissions from road traffic of Tokyo in 2015 are about 16,323 Gg. The highlight points mainly refer to linking the traffic census information for the observed road segments on map and allocation efforts for unserved ones. As the limited amount of observation points in traffic census, accurate estimation for unobserved road segments is a challenge. Our approach overcomes it by assumption of a familiar traffic condition for all road segments in the same sub-city area. This approach could simulate the traffic patterns closely to the real traffic condition so that it will more effectively support the emission mitigation policies on road traffic for local climate.

Calculation Method of Congestion Metrics based on Mobile Phone Signaling

With the increasingly serious congestion of the highway, it is very important to get the accurate traffic jam by the means of information technology. Under the condition of selecting the speed and traffic density as the congestion measure parameters, in order to effectively improve the accuracy of the results, we propose to eliminate the interference of off-highway vehicle users, adopt the method of the average vehicle mobile phone number and weighted speed calculated average traffic density and road speed, and describes its implementation process in detail. Finally, the results are compared with the data of the fixed detector on the highway, which indicates that the method proposed in this paper can meet the design requirements.

Consensus Filter-Based Distributed Variational Bayesian Algorithm for Flow and Speed Density Prediction With Distributed Traffic Sensors

This paper formulates a consensus filter-based distributed variational Bayesian (CFBDVB) algorithm for density approximation of traffic flow and average traffic speed in a freeway. This algorithm uses traffic measurements, including volume, occupancy rate, and average velocity, collected by some inductive loop sensors. These traffic sensors are settled in sealed spaces in the freeway network, such that they demonstrate a distributed sensor network. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. Using the suggested algorithm, a challenging problem is solved: selection of the right number of components. The algorithm begins with a large number of initialized components. In this algorithm, each sensor client severally computes local sufficient statistics by using local observations. A consensus filter is then used to spread out local sufficient statistics to neighbors and approximate global sufficient statistics in each client. Then, using the global sufficient statistics, irrelevant components are determined and omitted. Then, the remaining components' parameters are estimated. The suggested CFBDVB algorithm is scalable and robust. It estimates the parameters of mixtures and simultaneously selects the number of components. Various simulations of sensor nets have been done to confirm the promising performance of the CFBDVB algorithm.

Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks

Vehicle speed prediction provides important information for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging, because an individual vehicle speed is affected by many factors, e.g., the traffic condition, vehicle type, and driver’s behavior, in either deterministic or stochastic way. This paper proposes a novel data-driven vehicle speed prediction method in the context of vehicular networks, in which the real-time traffic information is accessible and utilized for vehicle speed prediction. It first predicts the average traffic speeds of road segments by using neural network models based on historical traffic data. Hidden Markov models (HMMs) are then utilized to present the statistical relationship between individual vehicle speeds and the traffic speed. Prediction for individual vehicle speeds is realized by applying the forward–backward algorithm on HMMs. To evaluate the prediction performance, simulations are set up in the SUMO microscopic traffic simulator with the application of a real Luxembourg motorway network and traffic count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.

Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos

Unmanned aerial vehicles (UAVs) are gaining popularity in traffic monitoring due to their low cost, high flexibility, and wide view range. Traffic flow parameters such as speed, density, and volume extracted from UAV-based traffic videos are critical for traffic state estimation and traffic control and have recently received much attention from researchers. However, different from stationary surveillance videos, the camera platforms move with UAVs, and the background motion in aerial videos makes it very challenging to process for data extraction. To address this problem, a novel framework for real-time traffic flow parameter estimation from aerial videos is proposed. The proposed system identifies the directions of traffic streams and extracts traffic flow parameters of each traffic stream separately. Our method incorporates four steps that make use of the Kanade-Lucas-Tomasi (KLT) tracker, k-means clustering, connected graphs, and traffic flow theory. The KLT tracker and k-means clustering are used for interest-point-based motion analysis; then, four constraints are proposed to further determine the connectivity of interest points belonging to one traffic stream cluster. Finally, the average speed of a traffic stream as well as density and volume can be estimated using outputs from previous steps and reference markings. Our method was tested on five videos taken in very different scenarios. The experimental results show that in our case studies, the proposed method achieves about 96% and 87% accuracy in estimating average traffic stream speed and vehicle count, respectively. The method also achieves a fast processing speed that enables real-time traffic information estimation.

Local government authority attitudes to road traffic CO2 emissions modelling: a British case study

ABSTRACTLocal government authorities (LGAs) play a key role in facilitating mitigation of road traffic CO2 emissions and must engage in emissions modelling to quantify the impact of transport interventions. Existing Emissions Model (EM) methodologies range from aggregate to disaggregate approaches, with more detail normally entailing more resources. However, it is not clear which approaches LGAs actually utilise. This article reports results of a survey designed to discover the level of detail considered practical by British LGAs (n = 34). Results show that resource scarcity is important, with particular importance attached to EM reusability and convenient input data sources. Most LGA EMs use traffic variable inputs (predominantly traffic flow and traffic average speed), with this approach being the best-fit for LGA resources. Link-by-link sources of data rated highly for convenience are Road Traffic Models and Urban traffic control systems.

Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information

With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area.

A distributed framework for network-wide traffic monitoring and platoon information aggregation using V2V communications

This study explores an innovative framework for distributed traffic monitoring and information aggregation using vehicle-to-vehicle (V2V) communications alone. We envision the proposed framework as the foundation to an alternative or supplemental traffic operation and management system, which could be particularly helpful under abnormal traffic conditions caused by unforeseen disasters and special events. Each equipped vehicle, through the distributed protocols developed, keeps track of the average traffic density and speed within a certain range, flags itself as micro-discontinuity in traffic if appropriate, and cross-checks its flag status with its immediate up- and down-stream vehicles. The micro-discontinuity flags define vehicle groups with similar traffic states, for initiating and terminating traffic information aggregation. The framework is validated using a microscopic traffic simulation platform VISSIM and its built-in component object model. Vehicle groups are successfully identified and their average speed and density effectively estimated. The impact of market penetration rate (MPR) is also investigated with a new methodology for performance evaluation under multiple traffic scenarios. Our simulation results show that the proposed framework lends itself better to more congested traffic conditions for any given MPR. With 50% MPR, the framework is able to provide information coverage for at least 37.76% of the simulated roadway facility under various traffic scenarios. This indicates that proposed framework could be useful with an MPR as low as 50%. Even with an MRP of 20%, the coverage ratio, under relatively congested traffic, can still reach around 58.82%. The framework is able to provide accurate speed estimation at high spatial resolution for the simulated roadway facility. The maximum relative error is under 10% for relatively congested traffic even with MPR as low as 20%. When there is a wider range of speed distribution (less congested traffic), the worst-case maximum relative error is still under 15% when MPR=20%. The density estimation is more sensitive to MPR, and is more accurate under low demand and high MPR scenarios. As expected, the accuracy of both speed and density estimation increases with MPR for any given traffic scenario.

Multimodal Intelligent Traffic Signal System Simulation Model Development and Assessment

The study demonstrated the multimodal intelligent traffic signal system (MMITSS) simulation tool to assess the potential effects of a broader MMITSS deployment that would ultimately facilitate the site-independent analysis of MMITSS applications. The study also identified the most beneficial operational conditions for each MMITSS operational scenario by considering a combination of simulation variables and traffic demand levels. The MMITSS simulation platform consists of the Vissim microscopic traffic simulation software, the basic safety message distributor program, an Econolite ASC/3 traffic controller emulator that runs on a Windows platform, and a roadside equipment module that runs on a Linux platform. The study demonstrated that the intelligent traffic signal system reduced vehicle delay by up to 35% and increased average traffic stream speed by up to 27%. In addition, transit signal priority (TSP) reduced travel time for both transit and passenger vehicles on the corridor by up to 29% and 28%, respectively. However, TSP can increase systemwide delay because it reduces green times on the side streets. Freight signal priority (FSP) can be used effectively along major freight routes. Although FSP significantly reduced truck delay, networkwide delay was increased substantially, especially in the scenario with a large percentage of trucks in the traffic composition. The TSP and FSP bundle simulation study demonstrated that the bundle operation successfully executed a hierarchical level of priority and provided higher priority for trucks. MMITSS applications effectively improve vehicle travel time and delay for equipped and potentially nonequipped vehicles, depending on the scenarios considered. Typically, systemwide disbenefits are observed when TSP, FSP, or both are applied.

Estimation of the generalised average traffic speed based on microscopic measurements

The average speed of vehicles plays an important role in traffic engineering. In almost any model-based traffic monitoring, analysis, or control application the average speed is required as a measure of performance or as an input for traffic models used to simulate fuel consumption, vehicle emissions, or traffic noise. The average speed is also used in algorithms that estimate the travel time. It also appears in the fundamental equation of traffic where density is calculated based on the average speed and flow. This article presents a new methodology for estimating the time–space–mean speed (TSMS), which is an equivalent for the generalised speed introduced by Edie [1963. “Discussion of Traffic Stream Measurements and Definitions”. Proceedings of the 2nd international symposium on the theory of traffic flow. Paris, France, 139–154]. To this aim, first tight upper and lower bounds are developed for the TSMS using individual vehicle speeds that are obtained via point measurements. To estimate the TSMS from these bounds, and to deal with the cases where the trajectories of the vehicles might not be straight lines, a convex combination of the upper and lower bounds is introduced. In order to assess the convex combination and to compare its performance with other formulas in the literature, two real-life data sets corresponding to the NGSIM data for the I-880 highway in the San Francisco Bay Area, and the A13 data near Rotterdam–Delft are used. At the end, MATLAB simulations are presented to cover scenarios that are not included in the available real-life data sets. The results produced by the new formula, both for the real-life data sets and for MATLAB simulations, are found to be more accurate compared with other available formulas in the literature.

Review, Experimental Evaluation and Policy Considerations of a Directional Time of Day Truck Restriction on Highways

The paper reviews several strategies of restricting or separating trucks from the regular traffic stream. Typical truck restriction policies focus on leftmost lanes restriction, which has been shown by several studies to have some advantages. However, those studies clearly show that vehicle queue lengths in the vicinity of critical merging areas increase significantly as the percentage of trucks increases. Therefore, this study examines a different policy—one which investigates traffic efficiency gained by restricting heavy truck traffic in one direction—in this case, westbound on Highway 1 in Israel—during afternoon peak hours. Similar policies of utilizing a specific vehicle category (e.g. passenger cars or trucks) in different daily time periods or physical separation of homogenous traffic of passenger cars in the inner lanes and mixed traffic in the outer lanes, were recommended in Italian motorways and in New Jersey Turnpike dual-dual freeways respectively.Highway 1 is a freeway connecting Jerusalem and Tel Aviv that passes by Ben-Gurion International Airport. The major objective of this study is to estimate the benefit of restricting truck traffic in the traffic stream according to three traffic-flow parameters: average travel time, total travel time, and average traffic speed. Analysis of the results, which consider the significant differences of 30-minute time period samples (“before-after” truck restriction), shows that prohibiting trucks in all lanes in one direction during the peak afternoon period of 16:00-18:00 improved all three traffic flow parameters by 8%-12%. Generally a steep grade from which truck traffic is banned is correlated with an improvement in traffic flow. In our case, Highway 1 road segments 1 and 2 and 4, which have steep grades (longitudinal grades), incorporated the most significant improvements in the traffic stream parameters examined.

Small-area spatiotemporal analysis of pedestrian and bicyclist injuries in New York City.

This study quantifies the spatiotemporal risk of pedestrian and bicyclist injury in New York City at the census tract level over a recent 10-year period, identifies areas of increased risk, and evaluates the role of socioeconomic and traffic-related variables in injury risk. Crash data on 140,835 pedestrian and bicyclist injuries in 1908 census tracts from 2001 to 2010 were obtained from the New York City Department of Transportation. We analyzed injury counts within census tracts with Bayesian hierarchical spatial models using integrated nested Laplace approximations. The model included variables for social fragmentation, median household income, and average vehicle speed and traffic density, as well as a spatially unstructured random effect term, a spatially structured conditional autoregression term, a first-order random walk-correlated time variable, and an interaction term for time and place. Incidence density ratios, credible intervals, and probability exceedances were calculated and mapped. The yearly rate of crashes involving injuries to "pedestrians" (including bicyclists) decreased 16.2% over the study period, from 23.7 per 10,000 population to 16.2 per 10,000. The temporal term in the spatiotemporal model indicated that much of the decrease over the study period occurred during the first 4 years of the study period. Despite an overall decrease, the model identified census tracts that were at persistently high risk of pedestrian injury throughout the study period, as well as areas that experienced sporadic annual increases in risk. Aggregate social, economic, and traffic-related measures were associated with pedestrian injury risk at the ecologic level. Every 1-unit increase in a standardized social fragmentation index was associated with a 19% increase in pedestrian injury risk (incidence density ratio = 1.19 [95% credible interval = 1.16 - 1.23]), and every 1 standardized unit increase in traffic density was associated with a 20% increase in pedestrian injury risk (1.20 [1.15 - 1.26]). Each 10-mile-per-hour increase in average traffic speed in a census tract was associated with a 24% decrease in pedestrian injury risk (0.76 [0.69 - 0.83]). The risk of a pedestrian or bicyclist being struck by a motor vehicle in New York City decreased from 2001 to 2004 and held fairly steady thereafter. Some census tracts in the city did not benefit from overall reductions or experienced sporadic years of increased risk compared with the city as a whole. Injury risk at the census tract level was associated with social, economic, and traffic-related factors.

The study of the effect of reducing input variables in the modeling process of Tehran-Saveh freeway accidents

Since Iran is among the countries where the rate of accidents caused by inattention to safety rules and factors affecting it always has been rising and according to the capabilities of neural network model in predicting accidents, the main purpose of this paper can be Using principal component analysis to identify factors influencing on modeling process Tehran-Saveh freeway accidents that is perhaps one of the country one of crowded freeway. In order to understand better the results obtained for determine the independent variables into the principal component analysis model, benchmarking KMO is used and variables such as traffic, petrol rationing and environmental variables were used as independent variables that has effect on the dependent variable accidents rate. In this study In addition to evaluation of the efficacy of artificial neural network model to model the regression of natural logarithm of the freeway traffic modeling, the accuracy of the models that built in accidents involving artificial neural network models and regression of natural logarithm in before and after the removal of less important variables studied and the results indicate that removal of minor factors of the PCA in the process of modeling, accuracy built models with fundamental changes did not go up and as a result it has been proved that the average daily traffic volume and average vehicle speed is the most important role in accidents of this freeways.

An averaging approach to estimate urban traffic speed using large-scale origin-destination data

Knowledge about the driving condition can be exploited by the energy management system of plug-in hybrid electric vehicle (PHEV) to optimise its fuel economy. However, estimating urban traffic speed can be a challenging problem, since the complexity of the road network requires a large amount of sensing coverage. GPS equipped vehicles have been used to address this problem, despite the drawback of high transmission cost due to the periodic locations reporting during the entire trip. This paper presents an alternative method to estimate urban traffic speed when only the trip distance, origin, destination and trip time are known, without the detailed information about the vehicle trajectory. Taxicab fleet data is used due to its wide area-coverage and large data availability. One month (15 million) observations of taxi origin-destination data in New York City is processed using cluster computers to create a traffic speed model of Manhattan at different times of day. The developed average traffic speed model is able to accurately predict the recorded trip duration from the taxicab data, and therefore, can be used to estimate energy consumption and optimise PHEV control.

교통 및 도로특성을 고려한 DSRC 교통정보 신뢰성 향상에 관한 연구

본 연구는 교통 및 도로의 특성을 고려하여 DSRC 교통정보의 신뢰성을 향상시키는 것을 목표로 하고 있다. 먼저, 본 연구에서는 도시고속도로에서의 DSRC 수집 데이터의 특성과 이상치 데이터 발생의 문제점을 분석하였다. 그 다음으로, 이상치 제거의 최적 방안을 적용하여 신뢰성 있는 교통정보를 생성하였다. 이상치를 제거한 후, 정확성 평가와 신뢰수준별 표본수의 적정성 여부에 대해서도 수행하였다. 그 결과, 평균절대오차백분율(MAPE)는 2.2%~9.7%이고, 평균제곱근오차(RSME)는 2.2~7.5로 분석되어, 실제 평균 통행속도와 매우 유사한 값으로 나타났다. 또한, 오전 오후 첨두시간대의 표본수는 신뢰수준 95%, 90%, 허용오차 5kph 범위에서 매우 적정한 것으로 분석되었다. This study aims at improving the Reliability of DSRC Traffic information considering Traffic and Road Characteristics. First of all, this study analyzed the characteristics of DSRC data on urban expressway and problems of outlier data occurrence. After then, this study produced reliable traffic information by using an optimal method of the Outlier-Filtering. After Outlier-Filtering, this study performed accuracy evaluation and appropriateness check for the number of samples per confidence level. As a result, it showed that the MAPE was between 2.2% and 9.7% and RSME was between 2.2 and 7.5 which are very similar figures to the actual average traffic speed. Also, The samples of both Am peak and Pm peak periods were analyzed to be appropriate at the confidence level of 95%, and 90% within the allowable error range of 5kph.