Planning Of Transportation Systems Research Articles

Device-free Vehicle Type Identification for Next Generations Intelligent Transportation System

With the substantial progress in the automotive industry, intelligent vehicle identification systems have become imperative tools for next generation transportation system planning. The advent of internet of vehicles and intelligent cellular vehicular networks has entered the transportation system into a new era of technology. In smart transportation system of beyond 5G/6G vehicular networks, vehicle type identification is of vital importance for ensuring safe operability of autonomous driving environ ments, electronic toll, and traffic management. It can be used in high efficiency roadway services, vehicle theft prevention, road condition monitoring, automatic driving, access control security, etc. During the recent years, channel state information (CS I) of WiFi signal has gained escalating traction for activity or gesture recognition, and person identification with improved robustness and classification accuracy. Therefore, in this research work, a WiFi based vehicle type identification system is proposed for beyond 5G/6G vehicular networks. In this novel approach, we start from vehicle detection using the variation in CSI of WiFisignals caused by the moving vehicle. For vehicle detection we simply apply a threshold based algorithm and then extract meaningful features. Afterwards, the vehicle type is identified using machine learning algorithms. This is a wireless model that enables commercial WiFi devices to identify different types of vehicles using CSI measurements. The presented framework can identify six different types of vehicles with an accuracy of 91.8%

Traffic flow model considering the dynamics prediction of the leading vehicle

Traffic flow modeling has become increasingly important in today’s society due to the growing number of vehicles on the roads, leading to congestion and traffic incidents. Mathematical modeling can aid in the planning and optimization of transportation systems, enhancing overall efficiency and safety. The main result of this study is a mathematical model that describes the dynamics of multiple vehicles, taking into account the prediction of the leading vehicle’s behavior. The model is formulated as a system of delay differential equations. A computer program has been developed based on this model, which simulates traffic flow in various road scenarios. The simulation results are consistent with observed data from real traffic flows.

An attempt to determine the impact of the implementation of autonomous vehicles on a larger scale on the planning of city transport systems

Purpose: This paper aims to explore the potential impact of autonomous vehicles (AVs) on urban planning, sustainable urban development, and tourism. Methodology: The paper is a conceptual study that reviews and synthesizes existing literature on AVs, urban planning, and tourism. It also uses case studies to illustrate the potential effects of AVs. Results: The widespread adoption of AVs is likely to have significant implications for urban planning, including changes in land use, infrastructure design, and transportation patterns. AVs may also contribute to sustainable urban development by reducing traffic congestion and air pollution. In the tourism sector, AVs could lead to spatial changes, new social inequalities, and changes in the overnight visitor economy. Theoretical contribution: The paper contributes to the understanding of the complex interplay between AVs, urban planning, and tourism. It highlights the need for urban planners and tourism stakeholders to consider the potential impact of AVs in their decision-making processes. Practical implications: The paper provides practical insights for urban planners, tourism stakeholders, and policymakers on how to prepare for and adapt to the widespread use of AVs. It also emphasizes the importance of considering the potential benefits and challenges of AVs in the context of specific cities and tourism destinations.

State-of-the-Practice of a State Utilization of Traffic Simulation: Case Study of Florida, U.S.

Traffic simulation tools are widely used in analyzing and enhancing the forecasting, planning, design, operations, and management of transportation systems. Despite the increasing use of traffic simulation through the years, there are still many needs and issues facing transportation agencies and modelers in their use of traffic simulation. These needs and issues have not been sufficiently surveyed and summarized in the literature. The goal of this study is to identify the current applications, needs, and issues of traffic simulation modeling based on a survey of public and private agencies in Florida, U.S. The study conducted an online survey of traffic simulation modelers in Florida, followed by phone interviews with a subset of the respondents to the survey. The survey addressed the types of the utilized traffic analysis and simulation tools, utilized simulation guidance, utilized traffic data and performance measures, calibration processes and resources utilized by agencies, the current practice of future year demand forecasting, and perspective on the simulation of emerging technologies. The results from this study will help understand the present situation of applying traffic simulation and future needs to enhance simulation modeling capabilities and practices.

Impact of risk perception and trust in autonomous vehicles on pedestrian crossing decision: Navigating the social-technological intersection with the ICLV model

In the rapidly evolving realm of transportation technology, the dynamic relationship between pedestrians and technological innovations has attained unprecedented importance. The complex social-technological intersection surrounding pedestrian road crossings has emerged as an attention for traffic safety. What distinguishes the contemporary urban environment is the rapid assimilation of intelligent transportation systems (ITS) into the transportation infrastructure, including technological elements such as autonomous vehicles, advanced surveillance systems, and smart infrastructure. To investigate how pedestrians perceive risks, trust technology, and make decisions in this era of technological progress, we designed a video-based questionnaire utilizing the stated preference (SP) methodology. We collected SP data from 589 Chinese pedestrians and employed an integrated choice and latent variable (ICLV) model to quantify the influence of risk perception and trust in autonomous vehicle (trust in AV), treated as latent variables, on their crossing decisions. Our findings indicate that the presence of autonomous vehicles significantly affects pedestrian crossing decisions. Specifically, an increase in the approaching vehicle speed and a decrease in the approaching vehicle distance increase the pedestrians’ tendency to choose not to cross the road, and the latent variables of risk perception and trust in AV strongly predict this phenomenon. The results of the scenario analysis show that, compared with overall pedestrians, middle-aged pedestrians and high-risk perception-level pedestrians are more conservative in their crossing decisions, but high levels of trust in AV improve pedestrians’ willingness to cross the street. Additionally, the pedestrian-related findings of this study at the social-technological intersection provide better understanding of the decision process and contribute to the planning and development of urban intelligent transportation systems.

Development of a Method for Assessing the Quality of Public Transport Services within Urban Transportation Planning Systems

Development of a Method for Assessing the Quality of Public Transport Services within Urban Transportation Planning Systems

Enhancing sustainable urban air transportation: Low-noise UAS flight planning using noise assessment simulator

Low-altitude flights of unmanned aircraft systems (UASs), such as drones, can cause substantial noise pollution in urban areas. This study aims to tackle the crucial issue of noise in UAS flight planning and analyze the feasibility of implementing low-noise urban flights at a micro level. We employ a virtual flight simulator to accurately model the UAS noise impact in realistic flights and optimize the flight path via a heuristic algorithm accordingly. The flight noise assessment incorporates a sound source modeling technique for practical UAS models and an efficient Gaussian beam tracing method for outdoor noise propagation in realistic environments. Case studies for flight simulations are conducted in a representative resident community and metropolitan area to evaluate the noise impact resulting from different flight paths. By comparing our approach with benchmark shortest-distance paths, we validate its effectiveness in significantly reducing en-route noise exposure, encompassing both instantaneous and continuous noise levels. This method holds great potential for contributing to the planning and management of urban air transport systems. It effectively mitigates noise impact, promoting quieter and more environmentally friendly UAS operations and sustainable urban transportation.

AN ASSESSMENT OF VEHICULAR TRAFFIC CONGESTION ALONG ABUJA-NASARAWA ROAD WITH EMPHASIS ON KARU BRIDGE TO AYA INTERCHANGE

Traffic congestion poses a substantial challenge to efficient road transportation, impacting travel times, fuel consumption, and overall productivity. The Abuja-Nasarawa Road, a critical transportation corridor, faces persistent congestion issues attributed to factors such as increased vehicle numbers, inadequate infrastructure, and poor traffic management. The escalating traffic congestion along Abuja-Nasarawa Road adversely affects the region's productivity and economic development. This study aims to analyze the traffic congestion along the Abuja-Nasarawa Road to improve decision-making regarding road use and transportation system planning. The research design combines qualitative and quantitative methods, utilizing a questionnaire and traffic data collection. The study focuses on the AYA Interchange – Karu Bridge section of the road, a critical stretch facing congestion issues. The methodology involves traffic data collection, including vehicle counts, traffic volumes, and average speeds. Results indicate varying traffic flow rates during morning and evening peak hours. Karu Bridge experiences the highest traffic congestion, with a flow rate of 5433 vehicles per hour during the morning. The qualitative analysis reveals the distribution of road users, with 40% being commercial drivers. The findings reveal a prevalence of traffic congestion attributed to the concentration of economic activities in the city center, leading to increased commuter influx. The study recommends measures such as maintaining and expanding the road network, introducing alternative transportation like a railway service, and completing infrastructure projects like the Apo-Karshi Road.

From Transportation Equity to Accessibility Capability: A New Framework to Guide Transportation Planning for Older People

Despite increasing attention to theories in transportation equity, few focus on specific social groups, such as older people. Scholars and practitioners need to better understand the implications that residential location, access to transportation, and technology resources have for older people's quality of life. Drawing on literature from urban planning, gerontology, and public health, I develop a framework, accessibility capability, that will support the planning of future transportation systems and neighborhoods to accommodate older people's travel needs. Scholars and practitioners can benefit from this critical synthesis of literature as they consider how to build more age-friendly cities and communities.

Optimizing Vertical Transportation in Buildings: Strategies for Efficiency, Safety, and User Experience

The design and planning of vertical transportation systems within buildings are critical components that profoundly impact the overall functionality, safety, and user experience. As urban landscapes evolve, with the construction of taller and more complex structures, the demand for efficient vertical mobility solutions becomes increasingly imperative. This abstract delves into key strategies for optimizing vertical transportation within buildings, encompassing considerations such as traffic analysis, smart systems, energy efficiency, and user experience. Key Words: vertical transportation, lifts, escalator, Round trip time, Waiting time, Rated speed.

SAN ANTONIO TRANSPORTATION PLANNING SYSTEM: MAPPING INFRASTRUCTURE CAPACITY AND COORDINATION

Abstract Ride Connect Texas and its partnering agencies seek to develop an expanded senior mobility management system supported by a One Call One Click transportation planning system. Its purpose is to enhance on-demand transportation system coordination and the application of technology, providing consumers, service providers, and mobility managers with direct access to comprehensive transportation information. GIS mapping is used to help develop a spatial awareness of the transportation challenges among older people and individuals with a disability and the current system capacity. Interviews with transit, non-transit, and health and human service agencies will reveal factors that influence coordination among providers and opportunities for capacity expansion. Surveys and focus groups of older adults and individuals with disability will show differentiating characteristics of riders and transportation needs and challenges. GIS mapping will allow us to geocode these findings relative to the user’s street address and zip code, illustrating variations in specialized client needs, service availability, travel behavior, and available amenities for basic daily living. Mapping will identify gaps in transportation services, hot zones revealing high user demand and low system capacity, and the ability to reduce fragmentation of service through seamless technology-based coordination. The mapping element informs the measurement of a community’s transportation system capacity for this user group. The value proposition of this tool is its ability to help any community measure its readiness toward developing a comprehensive implementation strategy for coordinated demand response transportation, including resource mobilization strategies, capacity augmentation, phasing of the system, and implementation timeline.

DEVELOPMENT OF MODE CHOICE BEHAVIOR MODEL FOR INTER-REGIONAL PUBLIC TRANSPORT– A CASE STUDY OF INDIA

Mode choice behavior of the inter-regional public transport passengers is important for proficient planning and operation of transport systems in developing countries. It is the responsibility of the authority to satisfy the demand for long regional trips having a significant movement of passengers by providing efficient public transport services. Most of the studies for the mode choice behavior of the passengers have been done for the urban mass transport within the urban conglomerate. The study aims to improve knowledge of the factor affecting passengers' decisions on the mode of travel for inter-regional public transportation in India. The Multinomial Logit (MNL) model and Artificial Neural Network (ANN) models among Gujarat State Road Transport Corporation (GSRTC) buses, Railways, and privately operated buses were developed for the trips between Surat city and Bhavnagar region of Gujarat, India. The final ANN model reflects a difference of attributes has 90.52% mode prediction capability against 70.11% of the MNL model. The developed model gives the proper insight for improving the transportation facilities in the region. It is revealed that the improvement in the service level and egress parameters are more important to attract travelers. The sleeper seat availability, lesser travel cost, lesser egress distance, and availability of night journey have been found important during travelers' thinking process to select the traveling mode. There are 13.1% and 20.1% rises in the probability of choosing GSRTC bus and train mode, respectively, with the improvement in the service level. Moreover, the GSRTC bus should improve its connectivity.

Optimal policy for scheduling automated guided vehicles in large-scale intelligent transportation systems

Automated electric mobility technologies have been increasingly applied to large-scale intelligent transportation systems (ITSs) to enhance productivity and efficiency. Advanced technologies have reshaped the traditional transportation systems and posed numerous challenges to the deployment and management of new ITSs. A major challenge in the real-life implementation of ITSs is how to manage a large number of automated objects in a cooperative manner. In this paper, we investigate the scheduling and routing problem of automated guided vehicles (AGVs) in a complicated ITS. Cost and efficiency are identified as the two crucial performance indicators of such a novel ITS. An easy-to-implement practical decision policy and a tailored particle swarm based solution method are designed for problem solving. In addition to the theoretical contributions, this paper also conducts a case study to validate the effectiveness and applicability of the proposed methodology, thus contributing to the planning and management of large-scale transportation systems by modeling, optimizing, and validating a new ITS deployed with AGVs.

On the relationship between the activity at point of interests and road traffic

The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth, safe, and green transportation. Therefore, effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management concepts. In contrast to classical traffic detection solutions, this study investigates the correlation between travelers' social activities and road traffic. The s's primary goal is to investigate the presence of the relationship between social activity and road traffic, which might allow an infrastructure-independent traffic monitoring technique as well. People's general activities at Point of Interest (POI) locations (measured as occupancy parameter) are correlated with traffic data so that, finally, proper proxys can be defined for link-level average traffic speed estimation. The method is tested and evaluated using real-world traffic and POI occupancy data from Budapest (District XI.). The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic, which holds promise for future practical applicability.

Examining associations with on-time performance and identifying relevant road network, demographic, socioeconomic and land use characteristics within the bus stop vicinity for proactive and reliable public transportation system planning

Growing population, rapid urbanization, and increasing travel demand emphasize the need for reliable public transportation systems and sustainable transportation planning. A reliable bus service fosters a more significant, satisfied, and committed base of users. This research focuses on examining the association between on-time performance (OTP) and road network, demographic, socioeconomic, and land use characteristics to identify relevant external factors for proactive and reliable public transportation system planning. The analysis was conducted at a bus stop level. Bus arrival/departure data from the Charlotte Area Transit System (CATS) was obtained. The road network, demographic, socioeconomic, and land use characteristics were captured within 0.25-mile and 0.50-mile buffers. Pearson correlation analysis was conducted to understand the association between OTP and road network, demographic, socioeconomic, and land use characteristics by day of the week and time of the day. The results show that OTP is associated with external factors such as the number of signalized or cul-de-sac/dead-end intersections, number of lanes, network density, population, income (median and total), and land use types related to residential and commercial/employment purposes within the bus stop vicinity. The findings provide vital insights for transit agencies to enhance scheduling, service and maximize benefits, effectively utilize available resources, plan, and provide equitable services to all riders.

Study on Community Detection Method for Morning and Evening Peak Shared Bicycle Trips in Urban Areas: A Case Study of Six Districts in Beijing

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities.

Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach

Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. Deep learning models provide researchers with powerful tools to achieve this task, but research in this area is still lacking. This paper thus proposes a novel deep learning architecture named Spatio-Temporal Multi-Graph Transformer (STMGT) to forecast the real-time spatiotemporal dockless scooter-sharing demand. The proposed model uses a graph convolutional network (GCN) based on adjacency graph, functional similarity graph, demographic similarity graph, and transportation supply similarity graph to attach spatial dependency to temporal input (i.e., historical demand). The output of GCN is subsequently processed with weather condition information by the Transformer to capture temporal dependency. Then, a convolutional layer is used to generate the final prediction. The proposed model is evaluated for two real-world case studies in Washington, D.C. and Austin, TX, respectively, and the results show that for both case studies, STMGT significantly outperforms all the selected benchmark models, and the most important model component is the weather information. The proposed model can help micromobility operators develop optimal vehicle rebalancing schemes and guide cities to better manage dockless scooter-sharing operations.

Quantum computing for transport network design problems

Transport network design problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in a redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP is generally modelled as a bi-level problem, which is known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as a bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved using quantum annealing on a D-Wave quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides significant computational benefit. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.

Robust Multicriteria Sustainability Assessment in Urban Transportation

Developing methodologies to facilitate the planning of sustainable transport systems for decision makers (DMs) is becoming more critical. This study proposed a methodological framework for sustainable urban transportation to make decisions during urban transportation’s design and planning stages. Urban transportation alternatives were evaluated by sustainability indicators that considered a triple bottom line approach’s environmental, economic, and social aspects. To choose the best alternative sustainable transportation scenarios, two multicriteria decision-making (MCDM) methods, for example, a hesitant fuzzy analytical hierarchy process (HF–AHP) and multiple attribute utility model (MAUT), were integrated. First, eight sustainable transportation indicators that considered data availability from the transport sector were selected. The weights of the selected indicators were calculated using an HF–AHP. These indicators included carbon dioxide (CO2) emissions, energy consumption, depletion of nonrenewable resources, operational and maintenance costs, fuel and taxes, the number of fatalities or injuries, and motor vehicles for public transport per 10,000 population. Finally, sensitivity analysis was applied to validate the robustness. Based on HF–AHP results, the number of fatalities or injuries was the most significant among the eight indicators, with a 0.158 normalized weight (Ni). The results of this integrated methodology highlighted that Alternative 11, which was dominated by low-motorized vehicles (low-MVs), was the best sustainable alternative and Alternative 1 was the worst sustainable alternative, which was dominated by high-MVs with 0.69 and 0.27 total utility values, respectively. Low-motorized urban transportation alternatives showed higher sustainable performances than the motorized and high-motorized alternatives. This study proposed a novel and robust methodology for decisions on sustainable urban transportation projects and renovating current urban transportation systems.

Passenger Counting in Mass Public Transport Systems using Computer Vision and Deep Learning

Estimating the number of people in vehicles and stations in public transport systems is crucial to improve service quality. The TransMilenio system in Bogota has serious drawbacks due to the lack of information in congestion situations. In this work we present a computer vision method that estimates the number of people in TransMilenio stations using deep learning techniques. We offer free use of the TransMilenio-Javeriana database with nearly 900,000 head labels on buses and stations. From these images a deep learning architecture tuned for crowd counting was trained to generate density maps around the heads in the scene. Several head count methods were evaluated on the density maps. After testing the method with 10,800 images, the results show a mean absolute error of 1 head per frame, equivalent to 11% relative error. The accuracy of this method is much better than its manual counterpart. This method is also scalable and low cost, which indicates that it has great potential to provide information for the planning and operation of public transport systems.