Local Transportation Agencies Research Articles

An analysis of the vulnerability of road networks in response to disruption events through accessibility indicators specification

ABSTRACT The objective of this paper is to propose an analysis of the vulnerability of road transportation networks in response to external shocks. Through the estimation of the accessibility and Link Importance Indicator (LII), a vulnerability road transportation analysis has been carried out and applied to a real case study. The case study comprises the 112 districts of the metropolitan area of the city of Naples, in the south of Italy. The main findings of this study are represented by the accessibility indicator introduced, which can be considered a measure to capture the vulnerability due to disruptions in the road network, and the LII, which can be used to identify critical roads. The proposed methodology represents a useful tool for local administrations, transportation agencies, and civil protection that need to deal with emergency situations, which are becoming more common.

Understanding Ride-Hailing Sharing and Matching in Chicago Using Travel Time, Cost, and Choice Models

Ride-hailing data is sparingly available throughout the U.S., which limits researchers’ understanding of the mode. Chicago is one of a few cities that have mandated ride-hailing companies to submit detailed trip data to their local transportation agency. The dataset is one of the few to contain trip-level attributes such as fare, travel time, and trip length. Most research using the Chicago dataset has focused on understanding why people use ride-hailing. This study focuses on why ride-hailing passengers choose shared over private trips and what influences the shared trips to be matched. Trips to/from airports are less likely to be shared. Trips to/from low-income areas are more likely to be shared. Longer shared trips are more likely to be matched, shared trips to/from dense areas are more likely to be matched, and shared trips between areas with a high number of shared trips are more likely to be matched. Matching an additional shared trip with another adds approximately 4 min to a trip. Ride-hailing users’ value of time is found to be $48.23 per hour. Understanding travel behavior is important for all modes of transportation including ride-hailing. The results of this paper can be applied to guide polices aiming to promote more sustainable transportation modes.

Review of utilization of short concrete noise barriers as noise an abatement strategy

The initial construction cost of barriers has led to considerable interest by local Transportation agencies and the Federal Highway Administration (FHWA) to assess alternatives such as short noise barriers. Many agencies have found that the utilization of short concrete safety barriers as a noise mitigation strategy is highly beneficial. However, not much evaluation data is available to satisfy the FHWA noise requirements or to satisfy the Noise Reduction Design Goal (NRDG) (Noise Barrier Acceptance Criteria Analysis Publication No. FHWA-HEP-16-017). This paper presents an extensive review of the utilization of short concrete noise barriers as a noise abatement strategy. The study also reviews the relevant research, methodologies, tools, and technologies that have been used in evaluating the reasonableness and feasibility of this strategy. Various modeling techniques that can realistically model the insertion loss of short concrete noise barrier were also reviewed. A few proprietary short noise barrier systems were also evaluated for their reasonableness and feasibility. The study reveals a state of the practice survey of all DOTs with respect to the adoption and implementation of short noise barrier. The paper ultimately highlights the effectiveness, cost, use and implementation methodology of short solid barriers as an effective noise abatement strategy.

Investigation of In Situ Thermal Properties and Early-Age Pavement Behavior in the Design and Performance Evaluation of Roller Compacted Concrete Pavement in Louisiana, U.S

The proven durability and high load-carrying capacity of roller compacted concrete pavement (RCCP), combined with its simple and cost-effective construction method and high placement speed, has created a great deal of interest from many states and local transportation agencies in the U.S. Roller compacted concrete (RCC) mixture uses less cement content and less water compared with conventional concrete mixtures, which reduces the total shrinkage strain and RCC set temperature during the hardening stage, resulting in a reduction of early-age deformation and stress developments in the RCCP. Currently, research on the early-age behavior and other thermal properties of RCCP has not been well documented. Therefore, investigations of early-age behavior and thermal properties such as coefficient of thermal expansion, ultimate shrinkage, and built-in curling are needed to understand the true behaviors of RCCP under real climatic conditions. On the other hand, the currently available RCC thickness design procedures, for example, Street-Pave and Pavement Designer, are in general short of flexibility in consideration of the combined effect of wheel and environmental loading on RCCP performance. The primary objective of this work is to investigate the early-age behavior of RCCP and to evaluate the effect of thermal properties on the design and performance of RCCP constructed in Louisiana.

Knowledge to action: Resilience planning among state and local transportation agencies in the United States.

The results of a nationwide survey of state and local agencies conducted in 2020 are summarized to understand priorities for improving transportation emergency management (EM) and resilience. Questions on EM activities, policies, and operations were answered by 190 respondents from across the country. In addition to comparing responses across transportation, EM, and other organizations (law enforcement, public works, health, environmental services, etc.), differences are analyzed based on agency size, location, experience, and hazard exposure. Respondents were also asked to prioritize EM activities for additional funding. Capabilities such as Emergency Planning, Traffic Control, Response Teams, and Situational Awareness emerge as priority areas. Approximately 60 percent of transportation agencies report updates of EM plans within the last 5 years compared to 70 percent of EM and 38 percent among other agencies. Four general linear models (GLMs) are developed and tested to determine the attributes of respondents who support improvements in (1) planning, (2) communications, (3) collaboration, and (4) transformation of agencies. An evaluative framework for measuring and managing resilience is developed and tested based on the responses. The analysis reveals diverse EM experiences, capabilities, and priorities, providing insight into future directions for sustaining and improving transportation resilience.

Integrating a Multi-criteria Route Optimization with ArcGIS for Gravel Road Data Collection

The ability to identify and collect low-volume roads’ data, especially gravel roads, is critical in transportation planning. Developing an effective method for gravel-road data collection allows both decision-makers and local agencies to efficiently obtain information regarding road conditions. In this study, a geographical information system (GIS)-based decision- support system was applied to assist local agencies in determining reliable access routes to all gravel roads in Wyoming. The routing criteria were developed using the Network Analyst tool of ArcGIS software based on the restrictions on average daily traffic (ADT) volume values, speed limits, driving distances and driving hours per day. Results indicated that the routing maps created by ArcGIS software were an easy-to-use method to plan and schedule data collection on gravel roads. The routing analysis provided a reliable means to minimize driving time and distance. The methodology developed in this study may be employed by local transportation agencies for road-maintenance purposes in rural areas. KEYWORDS: Low-volume roads, Gravel roads, Route optimization, ArcGIS, Data collection.

Truck industry classification from anonymous mobile sensor data using machine learning

Freight demand forecasting models used by federal, state, and local transportation agencies to predict future freight flows are often based on economic forecasts of industry growth and/or commodity production/consumption rates which are then used to estimate expected freight movement, e.g., truck volumes. Unfortunately, there is a lack of data connecting industry served and commodity carried to freight movements which limits the accuracy and usability of such models. While the private sector collects robust data on freight movements including commodity carried, when shared with the public sector, this data is anonymized to protect privacy. Thus, there is a critical need to re-identify industry served and commodity carried from anonymous freight movement data in ways that maintain privacy standards. To address this research gap, we developed a classification model using data mining and machine learning methods to predict industry served by a truck from daily activity patterns of trip and stop sequences extracted from truck Global Positioning System (GPS) data. A Weighted Random Forest (WRF) supervised machine learning model is used to predict five industry groups: farm products, mining materials, chemicals, manufactured goods, and miscellaneous mixed goods. The WRF model achieves 88% prediction accuracy and explicitly accounts for class imbalance. The model does not reveal fleet, driver, company, etc. and thus, we are able to provide necessary insight into the relationship between truck movement and economic (industry) forecasts without violating data privacy standards. Ultimately, our model allows large streams of truck movement data to be leveraged for freight travel demand forecasting.

Evaluation of mechanical degradation characteristics of recycled and natural aggregates under gyratory compaction effort

Due to the reduced availability and increased cost of stiff and durable natural aggregates, the use of recycled aggregates to replace natural aggregates has gained popularity. However, recycled aggregates are prone to carrying a greater potential for mechanical degradation (i.e., breakage or physical abrasion) than natural aggregates due to their compositions and two-phase structures, as they typically contain two different materials in their matrices. Since mechanical degradation of aggregates can adversely affect the long-term performance of pavement systems (stiffness, durability, etc.), using regression models to estimate the mechanical degradation characteristics of aggregates during the material selection process would bring significant benefits to state departments of transportation (DOTs) and local transportation agencies. In this study, the Gyratory Abrasion and Image Analysis (GAIA) method was adopted to evaluate the mechanical degradation characteristics of five different recycled aggregates and one natural aggregate. The index properties, compositions, and morphologies of the aggregates were determined to develop regression models for evaluating such characteristics of the aggregates. Overall, the recycled aggregates exhibited higher (as high as 4.3 times) mechanical degradation than the natural aggregate. The regression models showed that oven-dry (OD) specific gravity (Gs) and median roundness of aggregates can be used to predict the mechanical degradation characteristics of both recycled and natural aggregates.

Using Probe-Based Speed Data and Interactive Maps for Long-Term and COVID-Era Congestion Monitoring in San Francisco

Probe data that provide roadway speeds and travel times are increasingly being used for a variety of purposes in the transportation domain. A key use of these datasets has been roadway performance monitoring by state and local transportation agencies that are mandated to measure and report performance of their transportation networks. The San Francisco County Transportation Authority (SFCTA) monitors roadway performance as a part of the biennial Congestion Management Program (CMP) and primarily uses probe-based speed data for that purpose. Despite considerable savings in time and effort for data collection, integrating and processing the probe data still required a significant amount of manual work. This study highlights these challenges and proposes a data processing pipeline which includes an automated network conflation process, an efficient large data processing framework, and an interactive web-based visualization. In addition, all the scripts and code developed were made open source and are readily accessible from a public repository on GitHub. The value of the pipeline is demonstrated through the development of web-based interactive maps to monitor both long-term and short-term congestion in San Francisco. The short-term congestion monitoring application is timely given the spread of the COVID-19 pandemic and the region’s rapidly changing traffic conditions. Several valuable lessons learned from use of probe data for roadway performance monitoring are shared. Developing tools to ensure consistency of the data product and to reduce reliance on any one data vendor is of key importance.

Evaluating connected vehicle-based weather responsive management strategies using weather-sensitive microscopic simulation

The purpose of this study is to perform analysis, modeling, and simulation (AMS) to investigate the effectiveness of connected vehicle (CV)-based Weather Responsive Management Strategies (WRMS) to address safety concerns on freeway corridors under adverse weather conditions. This study investigates three CV-based WRMS applications: Forward Collision Warning (FCW), Early Lane Change (ELC) advisory, and Variable Speed Limit (VSL), designs operational alternatives for WRMS using CV data, and develops an AMS tool using a weather-sensitive microscopic traffic simulator to understand the effectiveness of the three WRMS under different scenarios. Various CV market penetration rates (MPR), weather conditions, and WRMS algorithm settings are tested in this study. The case study is based on a real-world freeway corridor, a segment of the I–80 Connected Vehicle Testbed in Wyoming. The simulation results show the effectiveness of selected WRMS applications and provide operational insights that state and local transportation agencies may use in future strategic planning and operations of their weather-responsive programs.

Integrated Herfindahl–Hirschman Index, Compromise Programming, and ε-Constraint Method For Multicriteria Performance-Based Transportation Budget Allocation

This paper introduces a new method for multicriteria performance-based transportation budget allocation. First, the Herfindahl–Hirschman Index (HHI) that has long been used in economics for measuring the level of monopolization in a marketplace is employed to derive the relative weights of multiple non-commensurable transportation performance criteria. Next, a compromise programming (CP) model is formulated to help transform the multicriteria optimization formulation for transportation budget allocation to a single-objective optimization model solvable by minimizing the Chebyshev distance to the ideal levels of performance targets associated with individual performance criteria. Finally, the ε-constraint trade-off analysis method is incorporated into the combined use of HHI and CP framework to iteratively derive the optimal decision outcome. Six-year data on candidate investment projects proposed for a U.S. state-maintained rural Interstate highway system along with data details of the available budget is used in a computational study for method application. Comparative analysis of decision outcomes is conducted with the STEP method for cross validation. It has revealed that the proposed HHI-CP ε-constraint method outperforms the existing method and can be adopted by state and local transportation agencies to carry out effective budget allocation.

Material application methodologies for winter road maintenance: a renewed perspective

Winter roadway operations, commonly known as snow and ice control operations, are one of the most critical functions of state, provincial, and local transportation agencies in cold regions. These operations aim to provide safety and mobility through the timely and effective application of materials and mechanical removal. The most common materials used are salt (sodium chloride, solid or liquid brine), magnesium chloride-based, calcium chloride-based deicers, agro-based additives and blends, and abrasives. In practice, the specific choice and application method and rate of these materials are dependent on pavement temperature, precipitation type, level of service goals, budget, and environmental sustainability considerations. Best practices of material application are designed to apply the right type and amount of materials in the right place at the right time. This review presents a literature review and agency interviews that were conducted to gather information about the use of materials, including types of materials, application strategies, application rates, and application equipment.

Predicting Oil Production Sites for Planning Road Infrastructure: Trip Generation Using SIR Epidemic Model

Drilling activity produces a significant amount of road traffic through unpaved and paved local roads. Because oil production is an important contributor to the local economy in the state of North Dakota, the state and local transportation agencies make efforts to support local energy logistics through the expansion and good repair and maintenance of transportation infrastructure. As part of this effort, it is important to build new roads and bridges, maintain existing road pavement and non-marked road surface conditions, and improve bridge and other transportation infrastructure. Therefore, the purpose of this study is to review previous oil location prediction models and propose a novel geospatial model to predict drilling locations which have a significant impact on local roads, to verify and provide a better prediction model. Then, this study proposes a SIR (susceptible–infected–recovered) epidemic model to predict oil drilling locations which are traffic generators. The simulation has been done on the historical data from 1980 to 2015. The study found that the best fit parameters of β (contact rate) and μ (recovery rate) were estimated by using a dataset of historical oil wells. The study found that the SIR epidemic model can be applied to predict the locations of oil wells. The proposed model can be used to predict other drilling locations and can assist with traffic, road conditions, and other related issues, which is a much needed predictive model that is key in transportation planning and pavement design and maintenance.

WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study

Public transportation system as an essential mode of travel has been investigated by local governments and transportation agencies to capture passengers’ travel behaviors. Despite their efforts, agencies especially in small to medium sized cities could not afford to collect such behaviors data due to significant costs associated with the data collection system. In this study, we presented a WiFi sensing system which makes such data collection feasible with low-cost devices. We demonstrated the WiFi sensing system’s applicability in estimating passengers’ origin-destination (O/D) travel and passengers’ bus stop waiting times via video validation. In addition, WiFi signal strength was analyzed to further improve accuracy of the system. To this end, sliding window algorithm was adopted to mitigate the randomness of mobile devices’ signals. Our small-scale proof of concept experiment was conducted at four bus stops along the main transit corridor in Charlottesville, Virginia. Results indicated that the system was able to re-identify 91% of bus passengers and passengers bus stop waiting time error was as small as 7 seconds. It is expected that the system can be a viable low-cost Internet of Things (IoT) solution for monitoring public transit system performance.

A Method for Selecting Strategic Deployment Opportunities for Unmanned Aircraft Systems (UAS) for Transportation Agencies

Unmanned aircraft systems (UAS) are increasingly used for a variety of applications by state Departments of Transportation (DOT) and local transportation agencies due to technology advancements, lower costs, and regulatory changes that have simplified operations. There are numerous applications (e.g., bridge inspection, traffic management, incident response, construction and roadway mapping) and agencies find it challenging to prioritize which applications are most appropriate. Important factors to consider when prioritizing UAS applications include: (1) benefits, (2) ease of adoption, (3) stakeholder acceptance, and (4) technical feasibility. These factors can be evaluated utilizing various techniques such as the technology acceptance model, benefit analysis, and technology readiness level (TRL). This paper presents the methodology and results for the prioritization of UAS applications’ quality function deployment (QFD), which reflects both qualitative and quantitative components. The proposed framework can be used in the future as technologies mature, and the prioritization can be revised on a regular basis to identify future strategic implementation opportunities. Numerous transportation agencies have begun to use UAS, some have developed UAS operating policies and manuals, but there has been no documentation to support identification of the UAS applications that are most appropriate for deployment. This paper fills that gap and documents a method for identification of UAS applications for strategic deployment and illustrates the method with a case study.

Multivariate time series analysis of traffic congestion measures in urban areas as they relate to socioeconomic indicators

Traffic congestion has significant adverse implications for the environment and economy. Many state and local transportation agencies have implemented traffic congestion management practices to alleviate the negative implications of urban traffic. One of the major drawbacks of traffic congestion management practices is that they do not account for socio-demographic and economic factors, which have a significant impact on traffic congestion. Understanding the influence of these factors is very crucial because they can help to communicate the system's performance management and target setting. Only a few studies analyzed the relationship between traffic conditions (e.g., traffic demand and vehicular traveling speed) with a limited number of socio-economic factors. Moreover, most of the existing models ignore the temporal and spatial autocorrelations of traffic congestion, which may significantly limit their reliability and effectiveness. This study is developed with the purpose of identifying the most relevant external factors that affect traffic congestion performance measures. To conduct the research, we have used three urban congestion performance measures collected from 51 metropolitan areas across the U.S. over a four-year period, 2013–2016: travel time index, planning time index, and congested hours. We have used multivariate time series models to account for the complex inter-relationships among the performance measures and socioeconomic factors to identify the most influential factors affecting system performance. We have finally developed predictive models to estimate the traffic congestion measures using these factors. The results of rigorous modeling show that the factors influencing the traffic congestion measures are monthly average daily traffic (MADT), the number of employed, rental vacancy rate, building permits, fuel price index, and Economic Conditions Index (ECI). The prediction models indicated that the effects of these factors are statistically significant and could be used to forecast future trends in three performance measures accurately.

Multidimensional resource allocation for freight transportation project planning and decision making

• Maximize present worth of total benefits subjected to budget and policy constraints. • Economic competitiveness only and economic competitiveness with equity models. • Difference between manual and scientific resource allocation. • Distribution of benefits among multiple dimensions of freight transportation. • Variation of benefits with budgets and equity parameters. This paper develops and compares four multidimensional resource allocation models aimed to prioritize freight improvement projects for regional, state, and local transportation agencies such that return on investment is maximized. The proposed models are based on economic competitiveness with and without mutual exclusiveness in location, and equity in opportunity and outcome. Multiple dimensions of the models include the transportation mode, performance measures, improvement type, geographic regions, policy criteria, and time. Results from a case study in the State of Tennessee show that project selection based on equity in outcome provides the optimal balance between benefits and their distribution among counties, while project selection based on equity in opportunity results in the lowest total return on investment.

A Note on Assessment of Transportation Infrastructure Data Management Practices

The amount of data being maintained by state departments of transportation (DOTs) and local transportation agencies is increasing steadily. Although data provide opportunities to facilitate decision making at transportation agencies, there are challenges involved in managing large and diverse data. This article provides an assessment of the maturity of three data management practice (stewardship, storage and warehousing, and integration) for 16 transportation data groups based on a survey of 43 DOTs in the United States. The assessment results show that data management practices at the monitoring and operations phases of transportation infrastructure life cycle are likely more mature than those at other phases. Inventory data, in particular, has the most mature data management practices. On the other end, real estate data and travel modeling data have the least mature data management practices. A comparison of the practices indicates that data stewardship is more mature than data integration and storage and warehousing practices.

Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model

Conventional four-step travel demand models, used by most metropolitan planning organizations (MPOs), state departments of transportation, and local planning agencies, are the basis for long-range transportation planning in the United States. Trip distribution—whether the trip is intrazonal (internal) or interzonal (external)—is one of the essential steps in travel demand forecasting. However, the current intrazonal forecasts based on a gravity model involve flawed assumptions, primarily due to a lack of considerations on differences in zone size, land use, and street network patterns. In this study, we first survey 25 MPOs about how they model intrazonal travel and find the state of the practice to be dominated by the gravity model. Using travel data from 31 diverse regions in the U.S., we develop an approach to enhance the conventional model by including more built environment D variables and by using multilevel logistic regression. The models’ predictive capability is confirmed using k-fold cross-validation. The study results provide practical implications for state and local planning and transportation agencies with better accuracy and generalizability.

Applications of Measurement Error Correction Approaches in Statistical Road Safety Modeling

Road safety modelers frequently use average annual daily traffic (AADT) as a measure of exposure in regression models of expected crash frequency for road segments and intersections. Recorded AADT values at most locations are estimated by state and local transportation agencies with significant uncertainty, often by extrapolating short-term traffic counts over time and space. This uncertainty in the traffic volume estimates, often termed in a modeling context as measurement error in right-hand-side variables, can have serious effects on model estimation, including: 1) biased regression coefficient estimates; and 2) increases in dispersion. The structure and magnitude of measurement error in AADT estimates are not clearly understood by researchers or practitioners, leading to difficulties in explicitly accounting for this error in statistical road safety models, and ultimately in finding solutions for its correction. This study explores the impacts of measurement error in traffic volume estimates on statistical road safety models by employing measurement error correction approaches, including regression calibration and simulation extrapolation. The concept is demonstrated using crash, traffic, and roadway data from rural, two-lane horizontal curves in the State of Washington. The overall results show that the regression coefficient estimates with a positive coefficient were larger and those with a negative coefficient were smaller (i.e., more negative) when the measurement error correction methods were applied to the regression models of expected crash frequency. Future directions in applications of measurement error correction approaches to road safety research are provided.