Daily Traffic Volume Research Articles

Application of Artificial Neural Networks and Singular-Spectral Analysis in Forecasting the Daily Traffic in the Moscow Metro

In this paper, we investigate the possibility of applying various approaches to solving the problem of medium-term forecasting of daily passenger traffic volumes in the Moscow metro (MM): 1) on the basis of artificial neural networks (ANN); 2) using the singular-spectral analysis implemented in the package “Caterpillar”-SSA; 3) sharing the ANN and the “Caterpillar”-SSA approach. We demonstrate that the developed methods and algorithms allow us to conduct medium-term forecasting of passenger traffic in the MM with reasonable accuracy.

An Improved Algorithm for Highway Design Hourly Volume Calculation

The designed hour traffic volume is an important parameter in the construction of highway toll station. It is usually calculated by using the ratio of the 30th traffic volume and annual average daily traffic volume. Based on toll data of Shaanxi highway, the method of calculating 30th hour traffic volume was improved in the paper. After deducting the national legal holiday period for the free passage of small passenger cars ， the ratio of hourly traffic volume in smooth and annual average daily traffic was obtained as the designed hour traffic volume coefficient. This algorithm is more scientific and accurate and easy to implement. It meets the requirement of highway toll station traffic volume forecast.

Evaluation of Collision Potential at Four- Legged Unsignalized Intersection Using Traffic Conflict Technique

The counting of traffic conflict allows the estimation of accident potential at a particular location such as at-grade intersection. The objective of this paper is to examine the evasive action of driver occurs with some frequency at Shao intersection which may result in potential accident. This paper applied traffic conflict techniques to evaluate collision potential at unsignalized intersection located at Shao. The morning and evening peak traffic flow was captured using a video camera for seven days and then analyzed by playing back the video on daily basis. The 100 sample size of observed conflict was based on 95% confidence level, 5% permitted error and the proportion of the vehicles that were involved in a specific conflict for the observed traffic flow. The conflicts identified were compared with volume counts at minor approach. The daily peak hour approach traffic volumes were found to have a close relationship with the percentage values of conflict on the two minor approaches. It also showed that rear end collision accounted for more than 70 percent of traffic conflicts and it correlated well with the traffic volumes on WB and EB approaches.

Evaluation of Collision Potential at Four- Legged Unsignalized Intersection Using Traffic Conflict Technique

The counting of traffic conflict allows the estimation of accident potential at a particular location such as at-grade intersection. The objective of this paper is to examine the evasive action of driver occurs with some frequency at Shao intersection which may result in potential accident. This paper applied traffic conflict techniques to evaluate collision potential at unsignalized intersection located at Shao. The morning and evening peak traffic flow was captured using a video camera for seven days and then analyzed by playing back the video on daily basis. The 100 sample size of observed conflict was based on 95% confidence level, 5% permitted error and the proportion of the vehicles that were involved in a specific conflict for the observed traffic flow. The conflicts identified were compared with volume counts at minor approach. The daily peak hour approach traffic volumes were found to have a close relationship with the percentage values of conflict on the two minor approaches. It also showed that rear end collision accounted for more than 70 percent of traffic conflicts and it correlated well with the traffic volumes on WB and EB approaches.

Modeling single-vehicle run-off-road crash severity in rural areas: Accounting for unobserved heterogeneity and age difference

This study investigates factors that significantly contribute to the severity of driver injuries resulting from single-vehicle run-off-road (SV ROR) crashes. A mixed logit model approach is employed to explore the potential unobserved heterogeneous effects associated with each age group: young (ages 16–24), middle-aged (ages 25–65), and older drivers (ages over 65). Likelihood ratio tests indicated that the development of separate injury severity models for each age group is statistically superior to estimating a single model using all data. Based on the crash data collected from 2009 to 2013 in North Carolina, a series of driver, vehicle, roadway, and environmental characteristics are examined. Both parameter estimates and their elasticities are developed and used to interpret the models. The estimation results show that contributing factors which significantly affect the injury severity of an SV ROR crash differ across three age groups. Use of restraint device and horizontal curves are found to affect crash injuries and fatalities in all age groups. Reckless driving, speeding, distraction, inexperience, drug or alcohol involvement, presence of passengers, and driving an SUV or a van are found to have a more pronounced influence in young and middle-aged drivers than older drivers. Compared to the passenger cars, older drivers are less likely to experience possible injuries in a large-size vehicle (e.g., truck or bus). The average annual daily traffic volume and lighting conditions are also found to influence the resulting injury severity of an SV ROR crash specific to young drivers.

Up on The 606

The 606 is the world’s first multiuse elevated trail, extending for 2.7 mi (4.35 km) through diverse neighborhoods whose per capita of open space is one of the lowest in Chicago. The trail connects six ground-level parks and is managed for recreation, but it also serves as a cross-town transportation connector and was funded partially with transportation dollars. Managers sought information about trail use to maintain a safe and harmonious experience for users, to plan operations and maintenance, and to document the benefits of trail development. The use of The 606 was examined during the first 6 months of 2016, and on the basis of those results, its use for the entire year was projected. Automated traffic monitoring with active infrared counters followed procedures in the FHWA Traffic Monitoring Guide. Screenline calibration tests revealed relatively high rates of occlusion owing to user type and traffic volume, yielding an adjustment factor of 1.239. Most users were pedestrians, but proportions varied by day of the week and time of day. Average daily traffic volumes between January 1 and June 30 at counters near the east and west ends of the trail were 3,500 and 3,000, respectively, with peak daily traffic exceeding 10,000. A regression model using weekdays and weekends, location on the trail, and temperature variables explained 80% of the daily use variation. Model extrapolation with historical weather averages estimated annual traffic volumes at 1.46 million and 1.3 million for the two sites, and a combined total annual miles traveled of 3.7 million (5.95 million km). Management implications and future research directions are highlighted.

Reliability Evaluation of a PSC Highway Bridge Based on Resistance Capacity Degradation Due to a Corrosive Environment

This paper presents a method for evaluating the reliability of an in-service highway bridge that considers the resistance capacity loss due to various corrosive environments. To demonstrate the application of the suggested method, a pre-stressed concrete-I (PSC-I) type girder was selected as a sample bridge. An analytical procedure was developed to quantitatively evaluate the performance degradation of a PSC-I girder bridge considering the traffic conditions, corrosive environment, and crack damage. The bridge performance was evaluated by considering traffic conditions, including the annual average daily traffic volume, heavy vehicle volume, and corrosive environment (mild, normal, and severe). To calculate the resistance capacity, all variables regarding the materials and sections were considered through probabilistic variances, Monte Carlo simulation, and the statistical characteristics of the resistance. The results showed that the performance degradation is sensitive to the important parameters of the traffic conditions and corrosive environment, which may decrease the structural reliability and lead to bridge failure. Cracks in a PSC-I girder may accelerate the performance degradation and affect the reliability level of the bridge. Therefore, a maintenance plan should be rationally considered depending on the site environment.

Exploring contributory factors to highway accidents: A nonparametric multivariate adaptive regression spline approach

ABSTRACTPoisson and negative binomial regression models have been commonly applied to identify the significant factors that affect accident frequency on highways or at intersections. This study explores the application of an alternate method, the nonparametric multivariate adaptive regression spline (MARS) technique, to identify the effects of highway geometric characteristics, traffic factors, and environmental conditions on the frequency of highway accidents. The 2007 to 2008 accident data for National Freeway 1 in Taiwan were collected to develop MARS models. Results indicate that horizontal alignment, vertical alignment, average daily traffic volume (ADT), heavy vehicle ADT, and precipitation all have nonlinear effects on highway accidents.

Sources of hydrocarbons in urban road dust: Identification, quantification and prediction

Among urban stormwater pollutants, hydrocarbons are a significant environmental concern due to their toxicity and relatively stable chemical structure. This study focused on the identification of hydrocarbon contributing sources to urban road dust and approaches for the quantification of pollutant loads to enhance the design of source control measures. The study confirmed the validity of the use of mathematical techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for source identification and principal component analysis/absolute principal component scores (PCA/APCS) receptor model for pollutant load quantification. Study outcomes identified non-combusted lubrication oils, non-combusted diesel fuels and tyre and asphalt wear as the three most critical urban hydrocarbon sources. The site specific variabilities of contributions from sources were replicated using three mathematical models. The models employed predictor variables of daily traffic volume (DTV), road surface texture depth (TD), slope of the road section (SLP), effective population (EPOP) and effective impervious fraction (EIF), which can be considered as the five governing parameters of pollutant generation, deposition and redistribution. Models were developed such that they can be applicable in determining hydrocarbon contributions from urban sites enabling effective design of source control measures.

A site-specific traffic load model for long-span multi-pylon cable-stayed bridges

This paper proposes a site-specific traffic load model for long-span multi-pylon cable-stayed bridge. Structural effects are primarily investigated based on influence lines, which are identified as either global effect (GE) or partial effect (PE) depending on the effective influenced region. GEs are further categorised as sensitive effect (SE), insensitive effect (ISE) or less sensitive effect (LSE), considering sensitivity to unbalanced traffic loading. Three on-bridge traffic states are simulated, and Weibull extrapolations are utilised to predict the extreme responses. These responses are analysed and compared with several design codes. Results indicate the maximum response is only 75% of the value calculated based on the design code of China (D60), and even lower than other codes. The responses show strong positive correlation with traffic parameters of annual average daily traffic volume and heavy vehicle proportion, and the on-bridge traffic states have significant influence on the responses. Further, the identified effects of ISE, SE and LSE present different responses, which indicate specific load models are needed accordingly. Finally, a site-specific traffic load model consisting of load form, loading pattern, multi-lane factor and load value is recommended, which gives an accurate illustration on the structural effects and traffic responses.

ETC technology and its Application in the Campus Intelligent Vehicle Management System

In order to solve the problem that the vehicles within the university campus are increasing steadily and a large number of outside vehicles drive through the campus arbitrarily and park disorderly. In this paper, a campus intelligent vehicle management system based on ETC is designed, and the ETC technology is introduced. On the basis of analyzing the system’s hardware architecture and workflow, a design model of lane is given. Finally, it discusses the communication theory of Winsock, customs a format of data frame and proposes a Winsock-based intelligent data synchronization algorithm. The daily traffic volume has been decreased by 65% since the system is put into operation in a university.

Toward a Better Estimation of Annual Average Daily Bicycle Traffic: Comparison of Methods for Calculating Daily Adjustment Factors

Several methods exist to calculate and apply adjustment factors for bicycle traffic. The reported accuracy of these factors differs from one study to another, making it difficult for transportation professionals to decide which method is preferable. The objective of this study is to compare the estimation accuracy of methods used to calculate daily adjustment factors and to quantify the performance of these methods relative to their mathematical intensity. Three methods of calculating daily adjustment factors were studied: the AASHTO method, the monthly and weather-specific method, and the day-of-year method. The estimation accuracy of annual average daily bicycle traffic volumes was assessed. The results supported the superiority of day-of-year factors, for which the mean absolute percent error (MAPE) was about 17.5%. Second was the monthly and weather-specific factors (MAPE ≈ 24.5%), and third was the AASHTO factors (MAPE ≈ 30.0%). Detailed error analysis was carried out to select the best days for bicycle volume data collection. The daily factor was modeled against day-specific attributes, such as weather conditions and day type, in an attempt to compute generic factors that are applicable to other locations. The model showed very good fit to the data with a coefficient of determination of about .87. The model was further used to develop daily factors for the validation data set and hence calculate annual average daily bicycle volumes. The MAPE was found to be 26.4% for all days and 20.5% for weekday data. The paper provides insights on the advantages and disadvantages of each calculation method and shows the priority preference of application.

Improved Annual Average Daily Traffic Estimation Processes

Annual average daily traffic (AADT) volume is estimated for federal, state, and local uses for roadway segments throughout the United States. The AADT estimates derived from permanent or portable counts are critical to roadway planning, system operations, and distribution of funding. The AASHTO AADT estimation formula is the most commonly used method, in part because it can be used under many circumstances when hourly traffic volume observations are missing, which is a common measurement issue with permanent traffic-counting sites. Despite its utility, the AASHTO AADT method has limitations in that it requires complete hourly data for any day that is included, and it does not account for variations in the numbers of each day of the week within a month or variations in the number of days within a month. This research evaluated new AADT estimation methods that incorporate days in which some, but not all, hourly observations are available and adjusts volume for the number of times each day of the week occurs in a month and the number of days per month. The bias and precision of four methods were examined for AADT estimation from hundreds of permanent traffic-counting sites. The analysis used more than 48 million records from the FHWA Travel Monitoring Analysis System, covering 14 years (2000 through 2013). This research quantified the improvement in accuracy and precision of the new methods, which effectively allow for a greater proportion of data to be retained in the estimation formula.

Verification of Methodical Procedure for Determining the Traffic Volumes Using Short-term Traffic Surveys

The traffic volume is frequently used measure of road capacity. Traffic volumes are suitable for urban planning, construction and upgrading of roadways or land-use planning decisions, while it is necessary to plan the construction of road infrastructure in the next few decades ahead on the basis of prognosis of road traffic.This paper outlines the problems of calculating the traffic volumes in Czech Republic. Especially the authors focus on the verification of methodical procedure for the determination of resulting traffic volumes using the short-term traffic surveys. The methodology TP 189 for determining the Annual Average Daily Traffic volumes (AADT) reflects the conversion coefficients which were statistically derived from long-term measurements of traffic intensities. It can be assumed that different traffic characters exist on different types of roads, for which no coefficients are set yet. Authors of the paper use data obtained from real long-term measurements of traffic volumes on the various categories of roads and try to determine categories of roads for which current conversion coefficients to determine AADT cannot be applied

Study on Safety of Railway/Highway Grade Crossings in the Philippines

This study aims to determine the crossing characteristics that contribute to accidents and propose mitigating procedures. Each railway/highway grade crossing was evaluated based on their existing characteristics namely: type of crossing, type of barrier, warning device, and the volume of traffic using the highway. Using hazard indices, each characteristic was evaluated individually. Two-way crossing has higher hazard index compared to one-way crossing, half barriers also exhibited higher hazard compared to full barriers. In the case of warning device, crossings with two different sets of warning device on each direction have exhibited the highest hazard index and 30-40 thousand for the vehicle traffic volume. Using chi-squared test, two combinations of characteristics passed the test, namely: the daily traffic volume versus barrier type and alarm device type versus crossing type. An assessment of all the characteristics for specific crossings was also made to evaluate the hazard index of a specific level crossing.

Heavy metals in soils along unpaved roads in south west Cameroon: Contamination levels and health risks.

Soils enriched with heavy metals from vehicular emission present a significant exposure route of heavy metals to individuals using unpaved roads. This study assessed the extent of Cd, Cr, Co, Cu, Ni, Pb and Zn contamination of soils along unpaved roads in Cameroon, and the health risks presented by incidental ingestion and dermal contact with the soils using metal contamination factor (CF) pollution load index, hazard quotients (HQ) and chronic hazard index (CHI). CF values obtained (0.9-12.2) indicate moderate to high contamination levels. HQ values for Cr, Cd and Pb exceeded the reference doses. Moderate health hazard exists for road users in the areas with intense anthropogenic activities and high average daily traffic (ADT) volume according to CHI values (1-4) obtained. The economy and quality of life in cities with unpaved roads could be threatened by health challenges resulting from long-term exposure to heavy metal derived from high ADT volumes.

Proof-of-concept for methodological framework to evaluate operational strategies with travel demand models

Recently, limited available resources for physical capacity expansion have generated supports for short-term operational improvements. Yet, only a few studies have dealt with evaluating these operational strategies effectively within the traditional transportation planning process even though suitable operational strategies impact to not only specific corridors or regions but also the whole transportation network. This is because it is generally perceived that integrating travel demand models with operational analysis approaches is quite difficult due to different constraints, modeling structures, and required data sets. In this regard, the concept of methodological framework to evaluate operational strategies with travel demand models is developed and validated by the proper case study (i.e., high occupancy toll lanes deployment in the Hampton Roads area in Virginia, US) in this research. The proposed framework consists of three major components: (i) the selection of an appropriate operational analysis approach, (ii) the disaggregation of daily traffic volumes to peak period volumes, and (iii) the alignment of modeling elements between the travel demand model and operational tool. Key contributions from this research are that (i) the proposed methodology enables the evaluation of travel behavioral changes without microscopic simulation, especially in terms of capturing network flow pattern changes caused by behavioral shifts after operational strategy deployment, (ii) the proposed framework eliminates assumptions required when only operational tools are used to evaluate operational strategies, (iii) the disaggregation method of a daily trip distribution matrix into peak period matrices by using survey data are developed, (iv) specific details influencing integration in terms of data types, peak period link capacity, volume-delay functions, and link impedance are identified. Consequently, even though this research still has some limitations (e.g., inherent weakness of travel demand models), this can be a starting point to develop more detailed guidelines as well as a good reference for practitioners and researchers who wish to evaluate operation strategies within transportation planning process.

Evaluation of the Efficiency of Neural Networks and Statistical Models to Determine Daily Traffic Volume of the Suburban Roads of Mazandaran Province

Realizing the traffic volume at the present time is frequently one of the concerns that occupies the planners’ minds in transportation. Knowing the current volume plays an important role in reflecting the performance of transportation system in the future. Traffic studies are based on observations and interpretations of the current circumstances .Since the present observations cannot be represented for the future status, it should be predicted by means of determined conditions. Annual Average Daily Traffic is one the measure to be used for the traffic volume, which has been mentioned in the codes. The fixed or non-fixed automated counters serve to count this volume. In Iran, Road Maintenance & Transportation Organization is responsible to count daily through different ways. In the present study, the data collected from the selected axes of Mazandaran Province was utilized to make a predictive model for traffic volume. It is fitted by data, linear and logarithmic regression models and also neural network model.

Road and traffic factors correlated to wildlife–vehicle collisions in Galicia (Spain)

Context Wildlife–vehicle collisions (WVC) are one of the major risk factors for the safety of drivers, as well as a great danger to wildlife that moves through the territory. In recent decades, given the growth of these accidents, some researches emerged to understand what are the main causes of this phenomenon and find the best solutions for implementation and try to solve this problem. Aims The aim of the present study was to analyse the road and traffic characteristics of road segments with a high occurrence of WVC in north-western Spain, specifically, the collisions with wild ungulates (roe deer and wild boar). Methods A nearest-neighbour analysis was used to analyse the spatial distribution of the WVC spots, and so as to identify these hotspots of accidents, we performed a hotspot analysis using the routine nearest-neighbour hierarchical cluster. Then, we calculated the WVC density of each road segment (KP). The existence of differences in the values of variables between high and low accident densities was analysed using a Mann–Whitney U-test for the continuous variables, and a χ2-test for the categorical ones. Then, multiple logistic regression analysis was used to identify which variables could predict the existence of KPs with a high density of WVC. Key results Our results showed that the daily traffic volume, the width of the road, the number of lanes and speed limit affect whether a particular road marker has a high or low density of WVC. Conclusions We conclude that high WVC is frequently characterised by wider lanes and shoulders, as well as gentler slopes, whereas in the sections with narrower roads and a shorter curvature radius, there are some conditions (low visibility and speed reduction) that reduce the probability of having an accident with ungulates. However, the speed at which it is possible to drive on a given road section is closely related to the occurrence of WVC. Implications These findings emphasise the importance of including mitigation measures in the decision-making when planning and designing infrastructure.

Corrigendum to: Road and traffic factors correlated to wildlife–vehicle collisions in Galicia (Spain)

Context Wildlife–vehicle collisions (WVC) are one of the major risk factors for the safety of drivers, as well as a great danger to wildlife that moves through the territory. In recent decades, given the growth of these accidents, some researches emerged to understand what are the main causes of this phenomenon and find the best solutions for implementation and try to solve this problem. Aims The aim of the present study was to analyse the road and traffic characteristics of road segments with a high occurrence of WVC in north-western Spain, specifically, the collisions with wild ungulates (roe deer and wild boar). Methods A nearest-neighbour analysis was used to analyse the spatial distribution of the WVC spots, and so as to identify these hotspots of accidents, we performed a hotspot analysis using the routine nearest-neighbour hierarchical cluster. Then, we calculated the WVC density of each road segment (KP). The existence of differences in the values of variables between high and low accident densities was analysed using a Mann–Whitney U-test for the continuous variables, and a ?2-test for the categorical ones. Then, multiple logistic regression analysis was used to identify which variables could predict the existence of KPs with a high density of WVC. Key results Our results showed that the daily traffic volume, the width of the road, the number of lanes and speed limit affect whether a particular road marker has a high or low density of WVC. Conclusions We conclude that high WVC is frequently characterised by wider lanes and shoulders, as well as gentler slopes, whereas in the sections with narrower roads and a shorter curvature radius, there are some conditions (low visibility and speed reduction) that reduce the probability of having an accident with ungulates. However, the speed at which it is possible to drive on a given road section is closely related to the occurrence of WVC. Implications These findings emphasise the importance of including mitigation measures in the decision-making when planning and designing infrastructure.