This study investigated the potential for development of station catchment areas around the proposed California High-Speed Rail System. The study was prompted by a review of practices of Japanese railway company groups that engage not only in train operations, but also in business diversification and property development within the station areas of their lines. These actions allow the company groups to diversify their revenues streams, increase ridership on their lines, and operate as a whole with net profits. This is in contrast to transit agencies in the United States, which only focus on transporting passengers along their lines and do not engage in other commercial activities. This situation limits the potential for transit in the United States to play a larger role in urban transportation. With the implementation of the California High-Speed Rail System, an opportunity exists to introduce the commercial transit model seen in Japan to the United States. Since the California High-Speed Rail System is a brand-new system with few entrenched interests to impede change, it has the potential to lead as an example of transit operating with net profits and providing additional benefits to the station areas it serves. However, since planning for station areas to turn into commercially successful activity centers is still a new concept and practice in the United States, a methodology has to be developed to assess the potential for development of station areas. This study set out to answer the two questions: 1) To what extent are the locations of the California High-Speed Rail System’s planned stations currently attractive to development within their respective contexts? 2) Given the information gathered from the study, what policies should be taken to enhance the future development potential of the California High-Speed Rail System’s planned stations as activity centers within their respective station areas? The potential for development was quantified by calculating accessibility indices for each station catchment area using the inputs of number of jobs, population size, and number of housing units within a gravity model. The results of the analysis indicate that the station areas at the ends of the alignment in the San Francisco Bay Area and the Los Angeles Metropolitan Area will benefit the most. The next biggest beneficiaries are the major population centers in the San Joaquin Valley, which are Fresno and Bakersfield. Other stations that are not likely enjoy the benefits of a high-speed rail connection as much as other stations are those that have very little development around them currently, such as Kings Tulare and Madera. However, the potential exists for all stations to enjoy substantial development opportunities if the proper plans, policies, and business strategies are implemented early on and at the corridor level to make the station areas attractive for development. The study makes the following recommendations: Promote the commercialization of train operations and station areas to capitalize on their long-term economic value; Integrate the planning, construction, ownership, and management of train operations and station area development and services to reduce transaction costs; Develop plans or business strategies for each station area to create roadmaps and timelines for their development; And plan for land use activities at station areas on a corridor level to capitalize on specific synergies between station origin-destination pairs (e.g., land use activities that accommodate long-distance travelers between the San Francisco Bay Area and the Los Angeles Metropolitan Area, or those that accommodate commuter needs between up-and-coming station areas in the San Joaquin Valley with major job centers).

Real-time crash prediction is a complex task, since there is no existing framework to predict crash likelihood, types, and severity together along with a real-time traffic management strategy. Developing such a framework presents various challenges, including not independent and identically distributed data, imbalanced data, large model size, high computational cost, missing data, sensitivity vs. false alarm rate (FAR) trade-offs, estimation of traffic restoration time after crash occurrence, and real-world deployment strategy. A novel spatial ensemble distillation learning modeling technique is proposed to address these challenges. First, large-scale real-time data were used to develop a crash likelihood prediction model. Second, the proposed crash likelihood model's viability in predicting specific crash types was tested for real-world applications. Third, the framework was extended to predict crash severity in real-time, categorizing crashes into four levels. The results demonstrated strong performance with sensitivities of 90.35%, 94.80%, and 84.23% for all crashes, rear-end crashes, and sideswipe/angle crashes, and 83.32%, 81.25%, 83.08%, and 84.59% for fatal, severe, minor injury, and PDO crashes, respectively, all while remaining very low FARs. This methodology can also reduce model size, lower computation costs, improve sensitivity, and decrease FAR. These results will be used by traffic management center for taking measures to prevent crashes in real-time through active traffic management strategies. The framework was further extended for efficient traffic management after any crash occurrence despite adopting these strategies. Particularly, the framework was extended to predict the traffic state after a crash, predict the traffic restoration time based on the estimated post-crash traffic state, and apply a three-step validation technique to evaluate the performance of the developed approach. Finally, real-world deployment strategies of the proposed methodologies for real-time crash prediction along with their types and severities and real-time post-crash management are discussed. Overall, the methodologies presented in this dissertation offer multifaceted novel contributions and have excellent potential to reduce fatalities and injuries.

No description available.

Pedestrian countdown signals (PCSs) are viable traffic control devices that assist pedestrians in crossing intersections safely. Despite the fact that PCSs are meant for pedestrians, they also have an impact on drivers’ behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers in the cities of Jacksonville and Gainesville, Florida. The study employs two Bayesian approaches, before-and-after empirical Bayes (EB) and full Bayes (FB) with a comparison group, to quantify the safety impacts of PCSs to drivers. Specifically, crash modification factors (CMFs), which are estimated using the aforementioned two methods, were used to evaluate the safety effects of PCSs to drivers. Apart from establishing CMFs, crash modification functions (CMFunctions) were also developed to observe the relationship between CMFs and traffic volume. The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings, using the EB approach indicated that installing PCSs result in a significant improvement of driver’s safety, at a 95% confidence interval (CI), by a 8.8% reduction in total crashes, a 8.0% reduction in rear-end crashes, and a 7.1% reduction in PDO crashes. In addition, FI crashes and angle crashes were observed to be reduced by 4.8%, whereas a 4.6% reduction in angle crashes was observed. In the case of the FB approach, PCSs were observed to be effective and significant, at a 95% Bayesian credible interval (BCI), for a total (Mean = 0.894, 95% BCI (0.828, 0.911)), PDO (Mean = 0.908, 95% BCI (0.838, 0.953)), and rear-end (Mean = 0.920, 95% BCI (0.842, 0.942)) crashes. The results of two crash categories such as FI (Mean = 0.957, 95% BCI (0.886, 1. 020)) and angle (Mean = 0.969, 95% BCI (0.931, 1.022)) crashes are less than one but are not significant at the 95 % BCI. Also, discussed in this study are the CMFunctions, showing the relationship between the developed CMFs and total entering traffic volume, obtained by combining the total traffic on the major and the minor approaches. In addition, the CMFunctions developed using the FB indicated the relationship between the estimated CMFs with the post-treatment year. The CMFunctions developed in this study clearly show that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Moreover, using the FB methodology, the results suggest the treatment effectiveness increased over time in the post-treatment years for the crash categories with two important indicators of effectiveness, i.e., total and PDO, and rear-end crashes. Nevertheless, the treatment effectiveness on rear-end crashes is observed to decline with post-treatment time, although the base value is still less than one for all the three years. In summary, the results suggest the usefulness of PCSs for drivers.

The safety at half-signalized intersections in Portland, Oregon is analyzed in this thesis using 10 years of crash history and analysis of video that was collected at a subset of intersections. A half-signalized intersection has a standard red-yellow-green traffic signal for automobiles on the major road, a stop sign for motorists on the minor road, and a pedestrian signal with actuation for pedestrians and/or bicyclists on the minor road. Although prevalent in Canada, this type of intersection control is not typically found in the United States because the MUTCD explicitly prohibits its use. Half-signal use is limited mostly to two cities in the Pacific Northwest. In Portland, Oregon there are forty-seven intersections where half-signals are used but the last installation was in 1986; Seattle has over 100 intersections with half-signals and installs these in new locations where warranted. To explore the safety records of these intersections in Portland, crash data from 2002-2011 was analyzed. A total of 442 crashes over the ten-year period at half-signals were observed. Sixteen of these 442 crashes involved pedestrians. In the crashes involving pedestrians, significant differences were found between the approach street of the vehicle and whether the pedestrian or driver was at fault. In the crash error reports, it was found that significantly more of the crashes involving pedestrians were the fault of motorists departing from the minor road who collided with pedestrians crossing the major street. Further crash analysis at half-signals was performed by developing matched comparison groups of minor stop controlled and fully signalized intersections. Crash rates were 0.158 and 0.178 crashes per million entering vehicles for 3-leg and 4-leg half-signals and these rates did not differ significantly from the minor street stop controlled and signalized comparison groups. Results from the matched comparison showed that the half-signalized group had more rear-end crashes when compared with the minor stop controlled group. This was the only result that held significance when crash rates were considered. It was also observed that the minor stop controlled group had a higher proportion of angle crashes when compared with the half-signal group but this did not influence the crash severity. Pedestrian crashes were more prevalent in the half-signal group when compared with the fully-signalized group. Pedestrian volumes were not available which would be used to determine if this significant measure is a result of higher pedestrian use at half-signals. In addition to crash analysis, video was captured at five half-signalized intersections totaling 180 hours. Traffic volumes, pedestrian and bicycle volumes, and signal actuations were collected over a twenty-four hour period. Over this twenty-four hour period the five intersections averaged daily counts of 18613 vehicles on the major street, 591 vehicles on the minor street, 263 pedestrians crossing the major street, 285 pedestrians crossing the minor street, 52 bicycles on the major street, 37 bicycles on the minor street, and 126 signal actuations. Twenty-four hour observations from each of the intersections were used to study conflicts and compliance. No conflicts were observed that reflect the left-turning from the minor street pedestrian crashes that were identified in the crash history. Compliance of the half-signal by vehicles and pedestrians was comparable to compliance at fully-signalized intersections found in other studies with one exception. Across the intersections where video was collected, consisting of four 4-leg intersections and one 3-leg intersection, seven left turn on red violations were observed which had a significant impact on the time after red that red light violations were made. It is hypothesized that at half-signals vehicles on the major street make a left turn on the red signal very late into the red phase because there is not a risk of colliding with a vehicle traveling on the minor street since traffic volumes on the minor street are comparably low. The observed left turn on red violations did not put pedestrians at risk since by that point into the signal pedestrians were already clear of the intersection. Finally, a stop compliance logistic regression model was developed at four four-leg intersections to see what factors had an effect on minor street vehicle stop compliance. All 166 hours of video were used to observe vehicles that arrived at the half-signal during the pedestrian phase. The dependent variable collected was whether a vehicle came to an acceptable stop. Independent variables collected included the vehicle's queue position, if it was the peak school period, if there was a vehicle across the street on the minor road, if a vehicle was stopped at the signal on the major street, if a pedestrian was present when the vehicle arrived, and the movement that the vehicle made from the minor street. Independent variables used in the model included the vehicle's queue position, if a vehicle was stopped at the signal on the major street, if a pedestrian was present, and if the vehicle made a right turn at the signal. Pedestrian presence and right turning vehicles had a positive impact on stop compliance. Vehicles being further back in the queue and cars stopped at the signal on the major street had a negative impact on stop sign compliance. In the model, pedestrian presence had the largest positive impact on stop compliance. When pedestrians were present, a motorist on the minor street was four times more likely to stop at the sign.

"Mammalian skeletal muscle is a very complicated biological structure to model due to its non-homogeneous and non-linear material properties as well as its complex geometry. Finite element discrete one-dimensional Hill-based elements are largely used to simulate muscles in both passive and active states. There are, however, several shortfalls to utilizing one-dimensional elements, such as the impossibility to represent muscle physical mass and complex lines of action. Additionally, the use of one-dimensional elements restricts muscle insertion sites to a limited number of nodes causing unrealistic loading distributions in the bones. The behavior of various finite element muscle models was investigated and compared to manually calculated muscle behavior. An improved finite element muscle model consisting of shell elements and Hill-based contractile truss elements in series and parallel was ultimately developed. The muscles of the thigh were then modeled and integrated into an existing 50th percentile musculo-skeletal model of the knee-thigh-hip complex. Impact simulations representing full frontal car crashes were then conducted on the model and the pre-loading effects from active thigh muscles on the femur were investigated and compared to cadaver sled test data. It was found that the active muscles produced a pre-load femoral axial force that acted to slightly stabilize the rate of stress intensification on critical stress areas on the femur. Additionally, the active muscles served to direct the distribution of stress to more concentrated areas on the femoral neck. Furthermore, the pre-load femoral axial force suggests that a higher percentage of injuries to the knee-thigh-hip complex may be due to the effects of active muscles on the femur. "

Transportation agencies are introducing new strategies and techniques that will improve traffic incident management. Apart from other indicators, agencies measure the performance of the strategies by evaluating the incidents timeline. An effective strategy has to reduce the length of the incident timeline. An incident timeline comprises various stages in the incident management procedure, starting when the incident was detected, and ending when there is the recovery of normal traffic conditions. This thesis addresses three issues that are related to the traffic incident timeline and the incident management strategies. First, co-location of responding agencies has not been investigated as other incident management measures. Co-location of incident responders affects the incident timeline, but there is a scarcity of literature on the magnitude of the effects. Evaluation of the co-location strategy is reflected by the response and verification durations because its effectiveness relies on improving communication between agencies. Investigation of the response and verification duration of incidents, before and after operations of a co-located Traffic Management Center (TMC) is done by using hazard-based models. Results indicate that the incident type, percentage of the lane closure, number of responders, incident severity, detection methods, and day-of-the-week influence the verification duration for both the before- and after- period. Similarly, incident type, lane closure, number of responders, incident severity, time-of-the-day, and detection method influence the response duration for both study periods. The before and after comparison shows significant improvements in the response duration due to co-location of incident response agencies. Second, the incident clearance duration may not necessarily reflect how different types of incidents and various factors affect traffic conditions. The duration at which the incident influences traffic conditions could vary – shorter than the incident duration for some incidents and longer for others. This study introduces a performance measure called incident impact duration and demonstrates a method that was used for estimating it. Also, this study investigated the effects of using incident impact duration compared to the traditionally incident clearance duration in incident modeling. Using hazard-based models, the study analyzed factors that affect the estimated incident impact duration and the incident clearance duration. Results indicate that incident detection methods, the number of responders, Traffic Management Center (TMC) operations, traffic conditions, towing and emergency services influence the duration of an incident. Third, elements of the incident timeline before the clearance duration have been overlooked as factors that influence the clearance duration. Incident elements before the clearance duration include verification time, dispatch duration, and the travel time of responders to the incident scene. This study investigated the influence of incident timeline elements before clearance on the extent of the clearance duration. Also, this study analyzed the impact of other spatial and temporal attributes on the clearance duration. The analysis used a Cox regression model that is estimated using the Least Absolute Shrinkage and Selection Operator (LASSO) penalization method. LASSO enables variable selection from incidents data with a high number of covariates by automatically and simultaneously selecting variables and estimating the coefficients. Results suggest that verification duration, response travel duration, the percentage of lane closure, incident type, the severity of an incident, detection method, and crash location influence the clearance duration.

The connected vehicle technology presents an innovative way of sharing information between vehicles and the transportation infrastructure through wireless communications. The technology can potentially solve safety, mobility, and environmental challenges that face the transportation sector. Signal phasing and timing information is one category of information that can be broadcasted through connected vehicle technology. This thesis presents an in-depth study of possible ways signal phasing and timing information can be beneficial as far as safety and mobility are concerned. In total, three studies describing this research are outlined. The first study presented herein focuses on data collection and calibration efforts of the simulation model that was used for the next two studies. The study demonstrated a genetic algorithm procedure for calibrating VISSIM discharge headways based on queue discharge headways measured in the field. Video data was used to first compute intersection discharge headways for individual vehicle queue position and then to develop statistical distributions of discharge headways for each vehicle position. Except for the 4th vehicle position, which was best fitted by the generalized extreme value (GEV) distribution, the Log-logistic distribution was observed to be the best fit distribution for the rest of vehicle positions. Starting with the default values, the VISSIM parameters responsible for determining discharge headways were heuristically adjusted to produce optimal values. The optimal solutions were achieved by minimizing the Root Mean Square Error (RMSE) between the simulated and observed data. Through calibration, for each vehicle position, it was possible to obtain the simulated headways that reflect the means of the observed field headways. However, calibration was unable to replicate the dispersion of the headways observed in the field mainly due to VISSIM limitations. Based on the findings of this study, future work on calibration in VISSIM that would account for the dispersion of mixed traffic flow characteristics is warranted. The second study addresses the potential of connected vehicles in improving safety at the vicinity of signalized intersections. Although traffic signals are installed to reduce the overall number of collisions at intersections, rear-end collisions are increased due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposed and evaluated an Advanced Stop Assist System (ASAS) at signalized intersections by using Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication. The proposed system utilizes communication data, received from Roadside Unit (RSU), to provide drivers in approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking upon a yellow and red signal indication. A simulation test bed was modeled using VISSIM to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that at least 60% penetration rate is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections. The last study addresses the fact that achieving smooth urban traffic flow requires reduction of excessive stop-and-go driving on urban arterials. Smooth traffic flow comes with several benefits including reduction of fuel consumption and emissions. Recently, more research efforts have been directed towards reduction of vehicle emissions. One such effort is the use of Green Light Optimal Speed Advisory (GLOSA) systems which use wireless communications to provide individual drivers with information on the approaching traffic signal phase and advisory speeds to arrive at the intersection on a green phase. Previously developed GLOSA algorithms do not address the impact of time to discharge queues formed at the intersection. Thus, this study investigated the influence of formed intersection queues on the performance of GLOSA systems. A simulation test-bed was modeled inside VISSIM to evaluate the algorithm’s effectiveness. Three simulation scenarios were designed; the baseline with no GLOSA in place, scenario 2 with GLOSA activated and queue discharge time not considered, and scenario 3 with GLOSA activated and where queue dissipation time was used to compute advisory speeds. At confidence level the results show a significant reduction in the time spent in queue when GLOSA is activated (scenarios 2 and 3). The change in the average number of stops along the corridor was found not to be significant when the base scenario was compared against scenario 2. However, a comparison between scenarios 2 and 3 demonstrates a significant reduction in the average number of stops along the corridor, and also in the time spent waiting in queues

Abstract Although researchers have studied the effects of platooning, most of the work done so far has focused on fuel consumption. There are a few studies that have targeted the impact of platooning on the highway operations and safety. This thesis focuses on the impact of heavy-duty vehicles (HDVs) platooning on highway characteristics. Specifically, this study aims at evaluating the effects of platooning of HDVs on capacity, safety, and CO2 emissions. This study is based on a hypothetical model that was created using the VISSIM software. VISSIM is a powerful simulation software designed to mimic the field traffic flow conditions. For model validity, the model outputs were compared with recommended values from guidelines such as the Highway Capacity Manual (HCM) (Transportation Research Board, 2016). VISSIM was used to obtain the simulation results regarding capacity. However, in addition to VISSIM, two other software packages were used to obtain outputs that cannot be assessed in VISSIM. MOVES and SSAM are two simulation software packages that were used for emission and safety metrics, respectively. Both software packages depended on input from VISSIM for analysis. It was found that with the presence of HDVs in the model, the capacity, the emission of CO2, and the safety of the roadway would improve positively. A capacity of 4200 PCE/h/ln could be achieved when there are enough HDVs in platoons. Furthermore, more than 3% of the traffic flow emission of CO2 reduction is possible when 100% of the HDVs used in the model are in platoons. In addition to that, a reduction of more than 75% of the total number of conflicts might be obtained. Furthermore, with the analysis of the full factorial method and the Design of Experiment (DOE) conducted by using Excel and Minitab respectively, it was possible to investigate the impact of the platoons’ factors on the highway parameters. Most of these factors affect the parameters significantly. However, the change in the desired speed was found to insignificantly affect the highway parameters, due to the high penetration rate. Keywords: VISSIM, MOVES, SSAM, COM-interface, HDVs, Platooning, Number of Conflicts

The implementation of managed lanes (MLs), also known as dynamically priced express lanes, to improve freeway traffic flow and personal throughput is on the rise. Congestion pricing is increasingly becoming a common strategy for congestion management, often requiring microscopic simulation during both planning and operational stages. VISSIM is a recognized microscopic simulation software used for analyzing the performance of managed lanes (MLs). This thesis addressed two important microscopic simulation issues that affect the evaluation results of MLs. One of the microscopic simulation issues that has not yet been addressed by previous studies is the required minimum managed lane routing decision (MLRD) distance upstream of the ingress point of MLs. Decision distance is an optimal upstream distance prior to the ingress at which drivers decide to use MLs and change lanes to orient on a side of MLs ingress. To answer this question, this study used a VISSIM model simulating I-295 proposed MLs in Jacksonville, Florida, United States (U.S), varying the MLRD point at regular intervals from 500 feet to 7,000 feet for different levels of service (LOS) input. Three measures of effectiveness (MOEs) - speed, the number of vehicles changing lanes, and following distance - were used for the analysis. These MOEs were measured in the 500 feet zone prior to the ingress. The results indicate that as the LOS deteriorates, speed decreases, the number of vehicles changing lanes increases, and the following distance decreases. When the LOS is constant, the increase in the MLRD distance from the ingress point was associated with the increase in the speed at the 500 feet zone prior to the ingress, less number of lane changes, and the increase in following vehicle gap. However, the MOEs approached constant values after reaching a certain MLRD distance. LOS D was used to determine the minimum MLRD distance to the ingress of the MLs. The determined minimum MLRD distances were 4,000 and 3,000 feet for 6 and 3 lane segments prior to the ingress point, respectively. Another issue addressed in this thesis is the managed lane evaluation (MLE) outputs, which include speed, travel time, density, and tolls. In computing the performance measures, the existing VISSIM managed lane evaluation (EVMLE) tool is designed to use the section starting at the point when vehicles are assigned to use MLs, also known as the MLRD point, which is located upstream of the ingress. The longer the MLRD distance from the ingress, the more the EVMLE tool uses the traffic conditions of the MLs traffic before entering the ML in its computations. This study evaluates the impact of the MLRD distance on the EVMLE outputs and presents a proposed algorithm that addresses the EVMLE shortcomings. In order to examine the influence of the MLRD distance on the outputs of the above-mentioned two algorithms, simulation scenarios of varying MLRD distances from 500 ft to 7,000 feet from the ingress were created. For demonstration purposes, only the speed was used to represent other performance measures. The analysis of variance (ANOVA) test was performed to determine whether there was a significant difference in the speed results with the change in the MLRD distance. According to the ANOVA results, the EVMLE tool produced ML speeds that are MLRD dependent, yielding lower speeds with an increased MLRD distance. On the other hand, the ML speed results from the proposed algorithm were fairly constant, regardless of the MLRD distance.