Transportation infrastructure and heat vulnerability

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Description
In the American Southwest, an area which already experiences a significant number of cooling degree days, anthropogenic climate change is expected to result in higher average temperatures and the increasing frequency, duration, and severity of heat waves. Climatological forecasts predict…

In the American Southwest, an area which already experiences a significant number of cooling degree days, anthropogenic climate change is expected to result in higher average temperatures and the increasing frequency, duration, and severity of heat waves. Climatological forecasts predict heat waves will increase by 150-840% in Los Angeles County, California and 340-1800% in Maricopa County, Arizona. Heat exposure is known to increase both morbidity and mortality and rising temperatures represent a threat to public health. As a result there has been a significant amount of research into understanding existing socio-economic vulnerabilities to extreme heat which has identified population subgroups at greater risk of adverse health outcomes. Additionally, research has shown that man-made infrastructure can mitigate or exacerbate these health risks. However, while recent socio-economic heat vulnerability research has developed geospatially explicit results, research which links it directly with infrastructure characteristics is limited. Understanding how socio-economic vulnerabilities interact with infrastructure systems is a critical component to developing climate adaptation policies and programs which efficiently and effectively mitigate health risks associated with rising temperatures.

The availability of cooled space, whether public or private, has been shown to greatly reduce health risks associated with extreme heat. However, a lack of fine-scale knowledge of which households have access to this infrastructure results in an incomplete understanding of the health risks associated with heat. This knowledge gap could result in the misallocation of resources intended to mitigate negative health impacts associated with heat exposure. Additionally, when discussing accessibility to public cooled space there are underlying questions of mobility and mode choice. In addition to captive riders, a growing emphasis on walking, biking and public transit will likely expose additional choice riders to extreme temperatures and compound existing vulnerabilities to heat.
Date Created
2016
Agent

Park-and-ride facilities design for special events using space-time network models

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Description
Given that more and more planned special events are hosted in urban areas, during which travel demand is considerably higher than usual, it is one of the most effective strategies opening public rapid transit lines and building park-and-ride facilities to…

Given that more and more planned special events are hosted in urban areas, during which travel demand is considerably higher than usual, it is one of the most effective strategies opening public rapid transit lines and building park-and-ride facilities to allow visitors to park their cars and take buses to the event sites. In the meantime, special event workforce often needs to make balances among the limitations of construction budget, land use and targeted travel time budgets for visitors. As such, optimizing the park-and-ride locations and capacities is critical in this process of transportation management during planned special event. It is also known as park-and-ride facility design problem.

This thesis formulates and solves the park-and-ride facility design problem for special events based on space-time network models. The general network design process with park-and-ride facilities location design is first elaborated and then mathematical programming formulation is established for special events. Meanwhile with the purpose of relax some certain hard constraints in this problem, a transformed network model which the hard park-and-ride constraints are pre-built into the new network is constructed and solved with the similar solution algorithm. In doing so, the number of hard constraints and level of complexity of the studied problem can be considerable reduced in some cases. Through two case studies, it is proven that the proposed formulation and solution algorithms can provide effective decision supports in selecting the locations and capabilities of park-and-ride facilities for special events.
Date Created
2016
Agent

Effect of roundabouts on accident rate and severity in Arizona

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Description
This study examines the outcomes of roundabouts in the State of Arizona. Two types of roundabouts are introduced in this study, single-lane roundabouts and double-lane roundabouts. A total of 17 roundabouts across Arizona were chosen upon several selection criteria and…

This study examines the outcomes of roundabouts in the State of Arizona. Two types of roundabouts are introduced in this study, single-lane roundabouts and double-lane roundabouts. A total of 17 roundabouts across Arizona were chosen upon several selection criteria and according to the availability of data for roundabouts in Arizona. Government officials and local cities’ personnel were involved in this work in order to achieve the most accurate results possible. This thesis focused mainly on the impact of roundabouts on the accident rates, accident severities, and any specific trends that could have been found. Scottsdale, Sedona, Phoenix, Prescott, and Cottonwood are the cities that were involved in this study. As an overall result, both types of roundabouts showed improvements in decreasing the severity of accidents. Single-lane roundabouts had the advantage of largely reducing the overall rate of accidents by 18%, while double-lane roundabouts increased the accident rate by 62%. Although the number of fatalities was very small, both types of roundabouts were able to stop all fatalities during the analysis periods used in this study. Damage rates increased by 2% and 60% for single-lane and double-lane roundabouts, respectively. All levels of injury severities dropped by 44% and 16% for single-lane and double-lane roundabouts, respectively. Education and awareness levels of the public still need to be improved in order for people to be able to drive within the roundabouts safely.
Date Created
2016
Agent

Development of PCI-based pavement performance model for management of road infrastructure system

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Description

The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The…

The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The objective of this research study was to develop a methodology for calculating a pavement condition index (PCI) based on historical distress data collected in the databases from Long-Term Pavement Performance (LTPP) program and Minnesota Road Research (Mn/ROAD) project. Excel™ templates were developed and successfully used to import distress data from both databases and directly calculate PCIs for test sections. Pavement performance master curve construction and verification based on the PCIs were also developed as part of this research effort. The analysis and results of LTPP data for several case studies indicated that the study approach is rational and yielded good to excellent statistical measures of accuracy.

It is believed that the InfoPaveTM LTPP and Mn/ROAD database can benefit from the PCI templates developed in this study, by making them available for users to compute PCIs for specific road sections of interest. In addition, the PCI-based performance model development can be also incorporated in future versions of InfoPaveTM. This study explored and analyzed asphalt pavement sections. However, the process can be also extended to Portland cement concrete test sections. State agencies are encouraged to implement similar analysis and modeling approach for their specific road distress data to validate the findings.

Date Created
2015
Agent

A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

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Description

It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion…

It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network-based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.

Date Created
2015-06-01
Agent

High occupancy toll lanes with a refund option: a stated preference survey of the Phoenix-Metropolitan Area

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Description
Managed Lanes (MLs) have been increasingly advocated as a way to reduce congestion. This study provides an innovative new tolling strategy for MLs called the travel time refund (TTR). The TTR is an “insurance” that ensures the ML user will…

Managed Lanes (MLs) have been increasingly advocated as a way to reduce congestion. This study provides an innovative new tolling strategy for MLs called the travel time refund (TTR). The TTR is an “insurance” that ensures the ML user will arrive to their destination within a specified travel time savings, at an additional fee to the toll. If the user fails to arrive to their destination, the user is refunded the toll amount.

To gauge interest in the TTR, a stated preference survey was developed and distributed throughout the Phoenix-metropolitan area. Over 2,200 responses were gathered with about 805 being completed. Exploratory data analysis of the data included a descriptive analysis regarding individual and household demographic variables, HOV usage and satisfaction levels, HOT usage and interests, and TTR interests. Cross-tabulation analysis is further conducted to examine trends and correlations between variables, if any.

Because most survey takers were in Arizona, the majority (53%) of respondents were unfamiliar with HOT lanes and their practices. This may have had an impact on the interest in the TTR, although it was not apparent when looking at the cross-tabulation between HOT knowledge and TTR interest. The concept of the HOT lane and “paying to travel” itself may have turned people away from the TTR option. Therefore, similar surveys implementing new HOT pricing strategies should be deployed where current HOT practices are already in existence. Moreover, introducing the TTR concept to current HOT users may also receive valuable feedback in its future deployment.

Further analysis will include the weighting of data to account for sample bias, an exploration of the stated preference scenarios to determine what factors were significant in peoples’ choices, and a predictive model of those choices based on demographic information.
Date Created
2015
Agent

A multi-sensor data fusion approach for real-time lane-based traffic estimation

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Description
Modern intelligent transportation systems (ITS) make driving more efficient, easier, and safer. Knowledge of real-time traffic conditions is a critical input for operating ITS. Real-time freeway traffic state estimation approaches have been used to quantify traffic conditions given limited amount…

Modern intelligent transportation systems (ITS) make driving more efficient, easier, and safer. Knowledge of real-time traffic conditions is a critical input for operating ITS. Real-time freeway traffic state estimation approaches have been used to quantify traffic conditions given limited amount of data collected by traffic sensors. Currently, almost all real-time estimation methods have been developed for estimating laterally aggregated traffic conditions in a roadway segment using link-based models which assume homogeneous conditions across multiple lanes. However, with new advances and applications of ITS, knowledge of lane-based traffic conditions is becoming important, where the traffic condition differences among lanes are recognized. In addition, most of the current real-time freeway traffic estimators consider only data from loop detectors. This dissertation develops a bi-level data fusion approach using heterogeneous multi-sensor measurements to estimate real-time lane-based freeway traffic conditions, which integrates a link-level model-based estimator and a lane-level data-driven estimator.

Macroscopic traffic flow models describe the evolution of aggregated traffic characteristics over time and space, which are required by model-based traffic estimation approaches. Since current first-order Lagrangian macroscopic traffic flow model has some unrealistic implicit assumptions (e.g., infinite acceleration), a second-order Lagrangian macroscopic traffic flow model has been developed by incorporating drivers’ anticipation and reaction delay. A multi-sensor extended Kalman filter (MEKF) algorithm has been developed to combine heterogeneous measurements from multiple sources. A MEKF-based traffic estimator, explicitly using the developed second-order traffic flow model and measurements from loop detectors as well as GPS trajectories for given fractions of vehicles, has been proposed which gives real-time link-level traffic estimates in the bi-level estimation system.

The lane-level estimation in the bi-level data fusion system uses the link-level estimates as priors and adopts a data-driven approach to obtain lane-based estimates, where now heterogeneous multi-sensor measurements are combined using parallel spatial-temporal filters.

Experimental analysis shows that the second-order model can more realistically reproduce real world traffic flow patterns (e.g., stop-and-go waves). The MEKF-based link-level estimator exhibits more accurate results than the estimator that uses only a single data source. Evaluation of the lane-level estimator demonstrates that the proposed new bi-level multi-sensor data fusion system can provide very good estimates of real-time lane-based traffic conditions.
Date Created
2015
Agent

A tour level stop scheduling framework and a vehicle type choice model system for activity based travel forecasting

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Description
This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model…

This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model systems in practice today are largely tour-based model systems that simulate individual daily activity-travel patterns through the prediction of day-level and tour-level activity agendas. These tour level activity-based models adopt a discrete time representation of activities and sequence the activities within tours using rule-based heuristics. An alternate stream of activity-based model systems mostly confined to the research arena are activity scheduling systems that adopt an evolutionary continuous-time approach to model activity participation subject to time-space prism constraints. In this research, a tour characterization framework capable of simulating and sequencing activities in tours along the continuous time dimension is developed and implemented using readily available travel survey data. The proposed framework includes components for modeling the multitude of secondary activities (stops) undertaken as part of the tour, the time allocated to various activities in a tour, and the sequence in which the activities are pursued.

Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice.
Date Created
2014
Agent
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