Automated Machine Learning Pipeline for Traffic Count Prediction

Research indicates that the projection of traffic volumes is a valuable tool for traffic management. However, few studies have examined the application of a universal automated framework for car traffic volume prediction. Within this limited literature, studies using broad data sets and inclusive predictors have been inadequate; such works have not incorporated a comprehensive set of linear and nonlinear algorithms utilizing a robust cross-validation approach. The proposed model pipeline introduced in this study automatically identifies the most appropriate feature-selection method and modeling approach to reduce the mean absolute percentage error. We utilized hyperparameter optimization to generate a universal automated framework, distinct from model optimization techniques that rely on a single case study. The resulting model can be independently customized to any respective project. Automating much of this process minimizes the work and expertise required for traffic count forecasting. To test the applicability of our models, we used Florida historical traffic data from between 2001 and 2017. The results confirmed that nonlinear models outperformed linear models in predicting passenger vehicles’ monthly traffic volumes in this specific case study. By employing the framework developed in this study, transportation planners could identify the critical links on US roads that incur overcapacity issues.

Probability-Based Static Truck Loading Model for Rural Bridges in Saskatchewan

A study was conducted to establish a new truck load model intended for the evaluation and design of bridges with simple spans of 20m or less located on rural roads in Saskatchewan. Monte Carlo simulation was used to generate truck data sets based on site-specific traffic conditions determined from a traffic count program conducted between 2008 and 2012 across all 296 rural municipalities, and data collected from six weigh-in-motion stations in the province from January to December 2013. All axle weights and spacings were modelled as probabilistic parameters. The critical truck configuration featured a truck tractor with a steering axle and tandem axle group, and a truck trailer with a tridem axle group. Truck models with a common axle configuration but varying weights were developed for various reference periods that reliably reproduced extreme nominal load effects over those periods. The use of other data sets may lead to different results.

Daily Traffic Count Imputation for Bicycle and Pedestrian Traffic: Comparing Existing Methods with Machine Learning Approaches

Monitoring nonmotorized traffic is becoming increasingly common practice at local and state departments of transportation. These travel activity data are necessary to monitor the system and track progress toward active transportation policy and program goals. A common problem is that permanent count site data are often missing, making those sites less useful. Being able to accurately estimate those missing data records functionally increases the amount of data available to use by themselves as metrics for monitoring traffic but also makes available more data for factoring short-term sites. Using nonmotorized traffic counts from several cities in Oregon, this research compared the ability of day-of-year (DOY) factors, a statistical model, and machine learning algorithms to accurately impute daily traffic records for annual traffic estimation. Based on exhaustive cross-validation experiments using data not missing at random scenarios, this research concluded that random forest and DOY factor approaches could be used to impute daily counts for nonmotorized traffic but each approach comes with tradeoffs. Though for many missing data scenarios random forest performed best, this method is complicated to estimate and apply. DOY factor-based methods are simpler to create and apply, and though more accurate in scenarios with significant amounts of missing data, they were less flexible given the need for data from neighboring count sites. Negative binomial regression was also found to work well in scenarios with moderate to low amounts of missing data. This work can inform nonmotorized traffic count programs needing vetted solutions for traffic data imputation.

A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.

OPPORTUNITY COST APPROACH EVALUATION OF URBAN TRAFFIC CONGESTION COSTS AT ORLU-ROAD BY WAREHOUSE INTERSECTION IN OWERRI METROPOLIS

This study estimated the economic cost of urban traffic congestion trip-time delay and extra vehicle operating cost at the Orlu-road by warehouse intersection in Owerri Metropolis, Imo state, Nigeria. Adopting the survey research design in which both questionnaire and observation of traffic (traffic count) were used as instruments of data collection, the study used the Willingness to Accept (WTA) method to estimate the opportunity cost of traffic congestion trip-time delay as the value which motorist attached to the extra travel time spent in congestion at the intersection. The extra vehicle operating cost was estimated using data obtained from the traffic count, the prevailing fuel cost in Nigeria and mathematical models derived from multiple literatures reviewed in the study. It was found that the aggregate economic cost of traffic congestion trip-time delay and extra fuel cost to the economy of Imo state runs into billions of naira for trip-time delays experienced

Extracting Densely Covered Areas Within Floating Car Datasets Using Inductive Loop Detector Data

We present an approach to use static traffic count data to find relatively representative areas within Floating Car Data (FCD) datasets. We perform a case study within the state of Nordrhein-Westfalen, Germany using enviroCar FCD and traffic count data obtained from Inductive Loop Detectors (ILD). Findings indicate that our approach combining FCD and traffic count data is capable of assessing suitable subsets within FCD datasets that contain a relatively high ratio of FCD records and ILD readings. We face challenges concerning the correct choice of traffic count data, counting individual FCD trajectories and defining a threshold by which an area can be considered as representative.

The Data-Driven Pattern for Healthy Behaviors of Car Drivers Based on Daily Records of Traffic Count Data From 2018 To 2019 Near Airports. A Functional Data Analysis

The road traffic injuries risk factors such as driving offenses and average speed are concerns for health organizations to reduce the number of injuries. Without any comprehensive view of each road, one cannot decide about the effective policy. In this manner, the data-driven policy will help to improve and assess the decisions. The count data near the road of two airports is surveyed for investigating the time-varying speed zones. The descriptive statistics, ANOVA, and functional data analysis were used. The hourly data of traffic counts for four different locations at the entrance of the two airports, international and domestics, were collected for one the year 2018 to 2019.The hourly pattern of driving offenses for each road was assessed and the to and from airport roads had different peaks (<0.05). The hour, weekdays, type of airport, direction and their interactions were statistically significant (<0.05) for the chance of driving offenses. The speed average during the day was statistically different (<0.5) by the number of different types of vehicles. The traffic count data is a great resource for decision making in safe driving subjects such as driving offenses. With functional data analysis, we can analyze them to get the most of the characteristics of this data. The airports are public places with high traffic demand in all countries that yields the different pattern of traffic transportation, therefore we extract the factors that affect the driving offenses. Finally, we conclude that conducting a time-varying speed zone near the airports seems vital.