Measuring Congestion for Strategic Highway Investment for Tomorrow (SHIFT) Implementation (PL-32)

The Kentucky Transportation Cabinet (KYTC) has moved toward a data-driven decision-making process, the Strategic Highway Investment Formula for Tomorrow (SHIFT), to allocate funds for highway improvement projects. SHIFT requires that candidate projects be scored on five critical metrics: safety, asset management, congestion, economic growth, and benefit/cost analysis. The measure of congestion used in SHIFT 2018 was a combination of volume-to-service flow ratio (VSF) and design hourly volume (DHV). VSF is a traditional performance measure developed based on limited data, primarily for sketch planning purposes. However, it does not accurately reflect the dynamics of traffic congestion of many facilities. This report presents a framework for integrating third-party speed data (acquired from HERE Technologies) into traditional congestion performance measures for use in SHIFT 2020. The speed data came from aggregated GPS-based vehicle locations at various temporal and spatial resolutions collected from 2015 to 2017. Data assessments undertaken by the research team found these data offer adequate coverages for monitoring congestion performance on most highways in Kentucky, except for some rural low-volume roads. An automated process was developed to conflate HERE’s proprietary network, to which the speed data are attached, and KYTC’s Highway Information System (HIS) network. Spatial integration lets the Cabinet link speed data to a state-maintained inventory database, enabling additional applications beyond those addressed in this study, such as the calibration and validation of travel demand models. The research team evaluated several performance measures that could potentially be applied in Kentucky. Based on this assessment, Vehicle Hours of Delay (VHD) is recommended as the best measure for quantifying congestion on a highway section. Two other measures – Vehicle Hours of Delay Per Mile (VHDPM) and Average Hours of Delay (AHD) – may be considered alongside VHD when performing network screening to identify bottlenecks. The research team, based on feedback from Cabinet work groups, developed a procedure for estimating VHD on highway improvement projects. A white paper in Appendix A documents this procedure

Genetic algorithm fuzzy clustering using GPS data for defining level of service criteria of urban streets

Developing countries like India need to have proper Level of Service (LOS) criteria for various traffic facilities as this helps in planning, design of transportation projects and also allocating resources to the competing projects. The LOS analysis for urban street followed in India is an adaptation of HCM-2000 methodology but the methodology is relevant for developed countries having homogenous traffic flow. In this research an attempt has been made to establish a framework to define LOS criteria of urban street in Indian context keeping in mind the geometric and surrounding environmental characteristics. Defining LOS criteria is basically a classification problem for which cluster analysis is a suitable technique can be applied. In this research a hybrid algorithm comprising of Genetic Algorithm (GA) and Fuzzy C-mean is utilized. As input to the clustering algorithm GA-Fuzzy a lot of speed data is required. From literature review GPS is found to be a suitable tool for collecting second by second speed data and GIS is suitable in handling large amount of speed data. The clustering algorithm is used twice in this study. First the GA-Fuzzy algorithm was used to classify Free Flow Speed (FFS) data into number of classes in order to get the FFS ranges of different urban street classes. To determine the optimal number of cluster using FFS data five cluster validation parameters are considered. After getting the FFS ranges for different urban street classes the same GA-Fuzzy algorithm is used on average travel speed data collected during both peak and off-peak hours to determine the speed ranges of different LOS categories. From this analysis the free flow speed ranges for different urban street classes and the speed ranges for different LOS categories are defined and the values are found to be lower than that suggested by HCM-2000. The coherence of the clustering result in classification of urban streets into four classes and speed values into six LOS categories is agreed with the physical and surrounding environmental characteristics of road segments under the study area. From this analysis it is also found that good LOS can’t be expected from urban street segment for which physical and surrounding environmental characteristics are not good

The urban real-time traffic control (URTC) system : a study of designing the controller and its simulation

The growth of the number of automobiles on the roads in China has put higher demands on the traffic control system that needs to efficiently reduce the level of congestion occurrence, which increases travel delay, fuel consumption, and air pollution. The traffic control system, urban real-time traffic control system based on multi-agent (MA-URTC) is presented in this thesis. According to the present situation and the traffic's future development in China, the researches on intelligent traffic control strategy and simulation based on agent lays a foundation for the realization of the system. The thesis is organized as follows: The first part focuses on the intersection' real-time signal control strategy. It contains the limitations of current traffic control systems, application of artificial intelligence in the research, how to bring the dynamic traffic flow forecast into effect by combining the neural network with the genetic arithmetic, and traffic signal real-time control strategy based on fuzzy control. The author uses sorne simple simulation results to testify its superiority. We adopt the latest agent technology in designing the logical structure of the MA-URTC system. By exchanging traffic flows information among the relative agents, MA-URTC provides a new concept in urban traffic control. With a global coordination and cooperation on autonomy-based view of the traffic in cities, MA-URTC anticipates the congestion and control traffic flows. It is designed to support the real-time dynamic selection of intelligent traffic control strategy and the real-time communication requirements, together with a sufficient level of fault-tolerance. Due to the complexity and levity of urban traffic, none strategy can be universally applicable. The agent can independently choose the best scheme according to the real-time situation. To develop an advanced traffic simulation system it can be helpful for us to find the best scheme and the best switch-point of different schemes. Thus we can better deal with the different real-time traffic situations. The second part discusses the architecture and function of the intelligent traffic control simulation based on agent. Meanwhile the author discusses the design model of the vehicle-agent, road agent in traffic network and the intersection-agent so that we can better simulate the real-time environment. The vehicle-agent carries out the intelligent simulation based on the characteristics of the drivers in the actual traffic condition to avoid the disadvantage of the traditional traffic simulation system, simple-functioned algorithm of the vehicles model and unfeasible forecasting hypothesis. It improves the practicability of the whole simulation system greatly. The road agent's significance lies in its guidance of the traffic participants. It avoids the urban traffic control that depends on only the traffic signal control at intersection. It gives the traffic participants the most comfortable and direct guidance in traveling. It can also make a real-time and dynamic adjustment on the urban traffic flow, thus greatly lighten the pressure of signal control in intersection area. To sorne extent, the road agent is equal to the pre-caution mechanism. In the future, the construction of urban roads tends to be more intelligent. Therefore, the research on road agent is very important. All kinds of agents in MA-URTC are interconnected through a computer network. In the end, the author discusses the direction of future research. As the whole system is a multi-agent system, the intersection, the road and the vehicle belongs to multi-agent system respectively. So the emphasis should be put on the structure design and communication of all kinds of traffic agents in the system. Meanwhile, as an open and flexible real-time traffic control system, it is also concerned with how to collaborate with other related systems effectively, how to conform the resources and how to make the traffic participants anywhere throughout the city be in the best traffic guidance at all times and places. To actualize the genuine ITS will be our final goal. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Artificial Intelligence, Computer simulation, Fuzzy control, Genetic Algorithm, Intelligent traffic control, ITS, Multi-agent, Neural Network, Real-time

Internet of Things-based Traffic Management System for Maseru, Lesotho.

Published ThesisThe number of vehicles in Maseru has been steadily increasing, leading to heightened intensity of congestion and traffic occurrences. This is further exacerbated by ineffective solutions that are currently in place as well as the absence of tools that facilitate dispersal of information to motorists. Traffic lights have been put in place to manage flow of traffic but are becoming increasingly inefficient due to their design. The preset timing cycles between green, amber and red disregarding prevailing conditions leads, inter alia, to increased wait times, use of additional fuel and air pollution. In addition, lack of equipment that is able to provide motorists with information about prevailing road conditions further increases the possibility of one being stuck in traffic. To make traffic management more efficient at signaled junctions, the implementation of the Internet of Things (IoT) paradigm is used to create intelligent traffic management systems such as Wireless Sensor Networks (WSN) and fuzzy algorithms to intelligently decide the phases of traffic lights. Road density and vehicles’ speeds are collected from the road infrastructure using cameras and are passed to a fuzzy algorithm to determine how congested a road is. Dependent on these parameters, the algorithm will also determine which roads should be given highest priority while maintaining a degree of fairness, thus optimizing traffic flow. In addition, the ubiquitous provision of road condition information to motorists in various formats such as text and audio is also used. This feature allows for the acquisition of the latest road status, thus making it possible to find alternative routes. The unique feature in this project is the ability to collect road parameters from the road infrastructure itself, using WSN as well as crowd source data from road users using mobile devices. A study conducted in this research revealed a relationship between the number of cars on a road and concentration of Carbon Dioxide (CO2); the results showed that as the number of cars increases, so does the measure of CO2. Questionnaire-based surveys showed that Maseru citizens have noted an increase in congestion which they attributed to the increase in number of vehicles on the road that is not met by the increase or improvement in road infrastructure. The respondents in this survey also noted limited mechanisms that provide them with road conditions and highlighted that such tools may alleviate congestion. The performance of intelligent traffic lights was conducted via simulations compared with fixed cycle traffic lights. From the simulations it was observed that IoT- based traffic management systems reduced the wait times of vehicles at signaled junctions which would also result in reduction of the pollutant CO2. It is envisaged that the future implementation will include the ability to manage a network of junctions and ability to predict abnormal traffic flows

Development of Level-of-Service Criteria based on a Single Measure for BRT in China

Bus rapid transit (BRT) has gained popularity as a cost-effective way of expanding public transit services, and its level of service (LOS) is receiving increasing attention. However, relatively little is known about the precise criteria that can consistently and objectively classify the LOS of BRT into different levels. This paper introduces the measure of “unit delay” to develop BRT LOS criteria, defined as the sum of delays a bus experiences at stops and intersections and on a 100m link. Based on field surveys conducted on BRT in Changzhou, China, we obtained a unit delay data set and established BRT LOS criteria using Fuzzy C-means Clustering. The LOS criteria can be applied for operational, design, and planning analyses for BRT systems. A method to examine the operational conditions in spatial and temporal dimensions and pinpoint the service bottlenecks of a BRT system is presented

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

"jats:p"Pedestrian level of service (PLOS) is an important measure of performance in the analysis of existing pedestrian crosswalk conditions. Many researchers have developed PLOS models based on pedestrian delay, turning vehicle effect, etc., using the conventional regression method. However, these factors may not effectively reflect the pedestrians’ perception of safety while crossing the crosswalk. The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data. Moreover, PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined. Therefore, it is an important attempt to develop a PLOS model with respect to pedestrian safety, convenience, and efficiency at signalized intersections. For this purpose, a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai, India. The data such as pedestrian, traffic, and geometric characteristics were extracted, and significant variables were identified using Pearson correlation analysis. A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis. PLOS was categorized into six levels ("jats:italic"A"/jats:italic"–"jats:italic"F"/jats:italic") based on the predicted user perception score, and threshold values for each level were estimated using the fuzzy "jats:italic"c"/jats:italic"-means clustering technique. The developed PLOS model and threshold values were validated with the field-observed data. Statistical performance tests were conducted and the results provided more accurate and reliable solutions. In conclusion, this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections. The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections. Document type: Articl