Transportation Management Plan Research Articles

Accurate crowd counting for intelligent video surveillance systems

The paper presents a novel deep learning approach for crowd counting in intelligent video surveillance systems, addressing the growing need for accurate monitoring of public spaces in urban environments. The demand for precise crowd estimation arises from challenges related to security, public safety, and efficiency in urban areas, particularly during large public events. Existing crowd counting techniques, including feature-based object detection and regression-based methods, face limitations in high-density environments due to occlusions, lighting variations, and diverse human figures. To overcome these challenges, the authors propose a new deep encoder-decoder architecture based on VGG16, which incorporates hierarchical feature extraction with spatial and channel attention mechanisms. This architecture enhances the model’s ability to manage variations in crowd density, leveraging adaptive pooling and dilated convolutions to extract meaningful features from dense crowds. The model’s decoder is further refined to handle sparse and crowded scenes through separate density maps, improving its adaptability and accuracy. Evaluations of the proposed model on benchmark datasets, including Shanghai Tech and UCF CC 50, demonstrate superior performance over state-of-the-art methods, with significant improvements in mean absolute error and mean squared error metrics. The paper emphasizes the importance of addressing environmental variability and scale differences in crowded environments and shows that the proposed model is effective in both sparse and dense crowd conditions. This research contributes to the advancement of intelligent video surveillance systems by providing a more accurate and efficient method for crowd counting, with potential applications in public safety, transportation management, and urban planning.

Estimation of dynamic Origin–Destination matrices in a railway transportation network integrating ticket sales and passenger count data

Accurately estimating Origin–Destination matrices is a pressing challenge in transportation management and urban planning. However, traditional methods like travel surveys have limitations in availability and comprehensiveness, which have been further exacerbated by the recent changes in mobility patterns induced by the COVID-19 pandemic. To address this issue, we focused on the Trenord railway network in Lombardy, Italy, and developed an innovative pipeline to integrate ticket and subscription sales and Automated Passenger Counting data using the Iterative Proportional Fitting algorithm. By effectively navigating the complexities of diverse and incomplete data sources, our approach showcases adaptability across various transportation contexts. Our research offers a valuable tool for operators, policymakers, and researchers, bridging the gap between data availability and the need for precise OD matrices. Additionally, we emphasise the potential of dynamic OD matrices and showcase methods for detecting anomalies in mobility trends, interpreting them in the context of events from the last months of 2022.

An activity space-based gravity model for intracity human mobility flows

Modeling and estimating urban human mobility flows is significant to multidisciplinary applications such as transportation engineering, urban planning and epidemic prevention. In recent decades, a wide range of trip distribution models for urban human mobility flows have been developed, among which the gravity model is the most classic and widely used. However, like most of the existing trip distribution models, the original gravity model adopts population as the proxy of urban region's attractiveness, which could not fully reflect the urban region's attractiveness as well as its temporal dynamics. Moreover, the original gravity model depends on the selection of proper distance decay functions, which require context-specific adjustable parameters derived from empirical mobility data. Taking advantage of the notion of activity space, we innovatively develop a proxy of urban region's attractiveness, and further propose a new activity space-based gravity (ASG) model. The case study results show that the ASG model significantly outperforms the original gravity model in estimating the overall human mobility flows and the hourly human mobility flows between urban regions. This study contributes to the state-of-the-art understanding of urban human mobility and human mobility flow modeling. The findings also provide insights into transportation management, infrastructure development and urban planning.

An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction

Traffic prediction is an important part of modern intelligent transportation systems (ITS), which helps transportation management and city planning. However, it is a very challenging task for modeling complex spatio-temporal dependencies, since the traffic data belongs to highly periodic multivariate time series which makes it hard to model accurate spatial dependencies only from time series and observed geolocation information of road segments. The existing research mainly focuses on finding ways of capturing dynamic spatial dependencies of road segments while neglecting the importance of periodicity, and few studies have explored a pure embedding-driven method that is robust to corrupted data to model periodicity. In this paper, we propose an embedding-driven multi-hop spatio-temporal attention network for traffic prediction (), which mainly focuses on leveraging the multi-scale periodicity of traffic data. Specifically, the proposed network applies a designed Fourier-series-based embedding, to capture the periodicity, which is more in line with real-world facts. Driven by the designed embedding, both local and global temporal dependencies are modeled properly by combining the attention-based methods and the convolution-based methods. Besides, we implement a trial that can hardly be seen in the existing traffic prediction works to combine the graph self-attention mechanism with a multi-hop diffusion process to explore the large-scale structural information on a designed set of graphs. Experiments on two real-world traffic datasets which contains traffic speed data for months show the effectiveness of our proposed methods. The experiments also suggest the methods can provide stable reasonable and smooth predictions for completely corrupted data.

Multi-graph fusion based graph convolutional networks for traffic prediction

Traffic prediction is significant for transportation management and travel route planning, and it is challenging as the spatial dependencies are complex and temporal patterns are dynamic. Local spatial dependencies exist between nodes nearby, and global spatial dependencies are between distant nodes with similar traffic patterns. One direct method to capture multiple spatial dependencies is to design a prediction model with multiple graph convolutional networks. However, it introduces high memory and parameter costs. The same with the learnable adjacency matrix-based approaches. Furthermore, existing methods are inefficient for temporal dependency modeling. To overcome such limitations, we propose a multi-Graph Fusion-based Graph Convolutional Network (GFGCN) for traffic prediction, where a multi-graph fused graph convolutional module is proposed without building multiple graph convolutional networks. The adjacency matrix in one graph convolutional network can reflect multiple spatial relationships through subspace merging on the Grassmann manifold. Moreover, a temporal module combined with the attention mechanism and a dilated convolutional network to model the temporal dynamic efficiently is designed. For validation and analysis, extensive experiments on three real-world datasets are performed. Experimental results show that the proposed GFGCN outperforms the baselines with better prediction accuracy.

Identify travel and health factors influencing well-being of the older adults-a case study in China.

With the increase in aging populations worldwide, the travel well-being of the elders has gained attention. The objective of this study is to examine the nonlinear relationships between the well-being of the older people in China and factors associated with travel and health. Based on the data collected in China, combined embedded feature selection and decision tree built by Gini index were utilized to screen for influential factors and to determine the importance of the features selected. Tamhane's T2 was used to study the differences in the important factors among older people with different levels of travel well-being. This study found that the travel well-being of older adults depends mainly on accessibility to public places, such as schools and medical facilities, and the availability of bus services. Out of expectation, the most important influential factor of travel well-being of older people is the distance from home to high school. This is related to the traditional Chinese concept of education. In addition, it was found that the body mass index is more important than self-perceived health as an influence factor of travel well-being of the elders in China. Social skills are important factors too. This study investigated various health-related and travel-related factors and their impacts on the travel well-being of older adults Chinese with the overall goal to improve the quality of life of the elders in China. The findings may provide a theoretical basis for the implementation of various transportation management and urban planning and design -related policies to improve the travel well-being of older adults in China.

Development of Transportation Models Based on Students’ Interest in a Parking Charging System at Universiti Malaysia Sabah (UMS)

Transportation management and sustainable transportation planning were critical. A well-planned transportation system is extremely beneficial in terms of efficiency and environmental friendliness. To that end, parking charging was one of the transportation management topics covered in this study. A parking charging system is one in which a user can leave their vehicle at a particular place and pay a price based on the amount of time it was left unattended. Given the rising use of private vehicles, which has resulted in an increase in congestion and air pollution, it is believed that a parking fee system can be implemented to alleviate the situation. The primary purpose of this research is to develop a transportation model based on the parking price factor in Ringgit Malaysia (RM). At the completion of the study, a transportation model based on parking rates will be developed, and it is projected that once implemented, the percentage of private vehicles that use public transportation will increase. This model is deemed necessary in order to mitigate the harmful effect of an excessive number of private vehicles at UMS. The State Preference Survey (SPS) method was used. A questionnaire form was developed and distributed online to 300 respondents among the students of the Faculty of Engineering at UMS, in order to collect the required data. The data collected was then analyzed using linear regression to develop several transportation logistic models. The transportation models that have been developed in the form of a logistic model that can reflect the willingness of UMS students to shift from private vehicles to public transport. These models predict that when the parking price increases, the percentage shift of private vehicles to public transport will increase linearly. It is also found that 100% of drivers are willing to shift from private vehicles to public transport if the parking price per hour is RM 4.00. Shifting private vehicle users to public transportation may assist lower the number of private vehicles on the road and thus indirectly help mitigate the negative consequences of an excess of private automobiles.

Visual exploration of urban functional zones based on augmented nonnegative tensor factorization

A city contains a variety of different urban functions with referring to the purpose of land use to support the diverse needs of urban residents, such as residence, working and recreation. Exploring urban functional zones is a critical task, which provides valuable applications for business site selection, transportation management and urban planning. It is well known that location information and human mobility semantics both are significant factors for identifying urban functional zones, just considering one factor is not effective. However, most of existing techniques capture the transformation of functional zones and interpret the results based solely on location or mobility semantics, and lack the capacity to deal with the multifaceted features of urban data. To tackle these problems, in this paper, we propose an interactive visual analytics system for effectively exploring urban functional zones based on spatio-temporal OD data and Points of Interest data. We first adaptively partition the territory into region units based on adaptive blue noise sampling method, extract POI feature matrix (location information) as a prior knowledge, model multidimensional spatio-temporal OD data as a tensor for addressing multifaceted features and propose an augment tensor-based algorithm that enables users to simultaneously combine mobility semantics and inherent location information for identifying functional zones. In addition, we design a set of visual encodings to better understand and interpret the results in a visual and intuitive manner. This system has been demonstrated using two case studies with a real-world dataset of HangZhou city and domain-expert interviews.

A new perspective for precision evaluation of large-scale traffic sensor data measurement

ABSTRACT Use of sensor data has been increasingly common in recent years, yet there is still a knowledge gap in evaluating the precision of traffic sensor data being used in traffic analyses for developing a transportation management plan. This paper fills this gap by exploring a new approach to evaluating the level of precision of large-scale traffic sensor data. The proposed analytical framework incorporates a spatiotemporal domain for the purpose of projecting spatiotemporal characteristics of the data into a repeatability and reproducibility (R&R) study. The main finding of this study is that the proposed framework is effective in examining the precision level of large-scale data spatiotemporally. The proposed framework would be useful for researchers and practitioners to benchmark precision measurements of traffic sensor data in a way to gather quality data and avoid any potential biased result of deeper traffic analyses.

Smart Transportation Management Planning to Reduce Surface Vehicle Pressure for a Better Urban Design of Howrah Municipal Corporation Area

The smallest district of West Bengal with great potential of growth, is highly influenced by its location on the of Kolkata, Howrah Municipal Corporation area faces the western bank of river Hooghly, just opposite to Kolkata, is the Howrah city. This city is growing since its inception and became an important part of the Kolkata conurbation. Transport infrastructure and services provide the basic framework for its regional development than other factors of development. From the last two centuries, the on-going transformation of the land use pattern of this district has been highly influenced by the transport development. The establishment of Howrah Railway Terminus (Government of West Bengal, 1967) was the main push factor of the then industrial sector in 1854. But the growing urbanization of the city nowadays pressurizes the man-land ratio which directly affects infrastructural sectors like transport. The existing road transportation system, railway networks, ferry system become unable to handle the pressure mainly during office hours on weekdays. Being a neighbour zone problem related to the transport and road pressure in rush hours. The metro service in Howrah Maidan is already started (2016), which is connected with Kolkata metro networks to reduce the surface transport pressure of the Howrah Municipal area. But still it is just at the foundation stage and only one connected node is not sufficient for the entire Howrah Municipal Corporation area. In this paper, we are going to propose for management of vehicles, probable new transport routes and auxiliary transport system to be a solution to overcome this problem on the basis of people's perception through our study.

Apriori-based text mining method for the advancement of the transportation management plan in expressway work zones

This study contributes to knowledge by advancing the transportation management plan (TMP) development efforts for expressway work zones. Using text mining techniques to a large-scale transportation data set that contains descriptively narrated texts, this research analyzes the association between words related to the type of work being performed and the type of lane closure in expressway work zone areas. It found that recurrent everyday tasks and bridge repair works tend to cause shoulder lane closure, while works—such as tunnel repair, night work, pavement, median barrier, road surface repair, and line marking—are more associated with main lane closure. Moreover, the findings further clarify the characteristic patterns shared between the number of closed lanes, and the respective lane position in two- and three-lane expressways. These offer significant insights into the decision-making process for the development of work zone TMPs, which can further be integrated into the various components of TMP to make the plan more effective and, at the same time, ensure an efficient throughput flow throughout the work zone, reduced congestion, and improved safety.

Work Zone Management in the District of Columbia: Deploying a Citywide Transportation Management Plan and Work Zone Project Management System

In a growing and congested capital city, the challenges of maintaining traffic during construction of a multimillion dollar transportation program are immense. Managers are tasked with budgeting, planning, designing, procuring, constructing, and monitoring hundreds of projects in a dynamic and politically charged environment. Critically important infrastructure, utility, maintenance, and developer projects—as well as capital area special events—are frequently on a national (or international) stage with a high risk of traffic congestion, safety concerns, and negative public perception. Washington, D.C., is often listed as one of the top 10 tourist destinations in the United States. With a growing resident population, commuters nearly double the number of people in the city each weekday. In addition, Washington, D.C., is often listed as the most congested urban area in the nation. Aside from recurring congestion, project construction and special events frequently strain the existing roadway network. A proactive, citywide approach to managing all work zones is helping to reduce congestion and provide the District Department of Transportation with better control of how best to schedule and approve projects, as well as budget for cost-effective strategies to mitigate work zone congestion. A unique Citywide Transportation Management Plan, including a dynamic Work Zone Project Management System, has been deployed to coordinate and analyze project work zones and special events throughout the city for the next 5 years. Graphical, web-based software tools track and assess cumulative work zone effects. From these results, mitigation strategies are aimed at improving safety and mobility throughout Washington, D.C.

The structural overview of root and branch concept in transportation management planning

This paper looks at the nature, structure, characteristics, scope and dimensional trends and operational functioning of root and branch concept of transportation management planning in any organization. The root and branch concept of transportation management planning is a process that connotes a managerial initiative of adopting a holistic bottom-up and top-down approach in managerial planning process. This concept is one that decides a systematic adoption of either bottom-up strategy or top-down approach in the management planning of a transport organization. This concept has a global view and honestly sees it as one that cant be possible without the management of the internal operational mechanisms and dynamics at the end. Therefore, effort should be made to have a holistic structural overview of this emerging concept in dealing and managing internal issues in the organization. Keywords: Structural Overview, Root and Branch Concept, Transportation Management Planning, Bottom-Up and Top-Down Approach.

Estimating Public Transportation Demand for Nakhon Ratchasima, Thailand

Nakhon Ratchasima was one of the two cities selected by German International Cooperation (GIZ) and Thailand's Pollution Control Department (PCD) to develop a draft of activities toward a Clean Air Plan. Identified as one of the most crucial source of pollution, traffic and transportation activities became one of the main focal points in this study. Focusing on promoting the use of public transportation, the study team investigated service characteristics, quality, ridership and trends in mode share in Greater Nakhon Ratchasima. It identified the role of public transport relative to other modes and current trends in mode share. This paper presents survey techniques with limited staff and time resource in survey and rational estimation. Against the operators' low ridership claim, the study team estimated rather promising passenger volume that could support decent quality public transport. The strengths and weaknesses of the current system were highlighted along with data gaps. The study team proposed a comprehensive but indicative range of practical short term public transport improvement and complementary options. Other supplement transportation management plan were also developed including traffic direction and parking regulations. The ending results would be reduction in travel time, pollution and energy consumption.

The practice of risk governance: lessons from the field

In contradistinction to generic and formal risk governance models such as the IRGC framework, this paper advocates the relevance of a contextual and practice-based approach to organizational risk governance. Three cases illustrate the socially situated dynamics of risk governance practice: public transportation management, river management, and railway planning. Risk governance is shown to derive from how actors, in their daily activities, mediate multi-level and regulatory institutional constraints, and solve actual problems through routines, trust, mutual understanding and not least, shared commitment to the societal role of infrastructure. Our findings underscore that risk governance takes place in contexts that are historically, spatially and institutionally situated. We therefore suggest that one needs to pay attention to the characteristics of this contextuality to understand the social dynamics of governance.

Strategy Implementation by State Transportation Agencies to Maintain Work Zone Mobility and Safety

Mitigation strategies for work zone congestion are used by transportation agencies to manage or reduce congestion through a work zone as part of the project's transportation management plan. The objective of this research is to assist agencies’ strategy selection by identifying strategies used by state transportation agencies (STAs) nationwide and their experiences. An online survey was developed to identify current, past, and future strategy implementation for projects similar to four developed scenarios: rural reconstruction, rural resurfacing, urban reconstruction, and urban resurfacing projects. The survey received 42 responses from individuals representing 28 states (responses were received from multiple districts or regions from a few states). It was found that agencies used several strategies to address different aspects of mobility and safety. Strategy selection varied between facility location and work activity. STAs also provided strategies that failed to meet expectations and identified strategies their agency plans to implement in the future. Based on the survey results and gaps in current literature, recommendations for strategy selection and future research are provided.

Transportation Management Plan Update

In January 1997 the Bellevue, Washington, office of CH2M HILL, a full service engineering consulting firm, implemented a new transportation management plan (TMP), replacing an innovative TMP implemented in 1987 that had gained attention nationwide. The results were remarkable: drive-alone commuting by CH2M HILL employees dropped from 61 to 40 percent in just a few months’ time. An employee-led committee adopted the new TMP before an increase in parking fees, a condition of the company’s building lease. The committee desired to offer employees an alternative to higher parking fees. The new TMP included King County Metro’s transportation benefits package called the FlexPass, along with a host of other incentives. The combination of higher parking fees and very inexpensive access to transit and vanpools led to a successful update to an already effective program. It is important to note that the tools were largely in place to support the mode shift: The CH2M HILL office is located within a city block of the downtown Bellevue transit center; an extensive park-and-ride system supports the transit center; the building is equipped with bike racks, showers, and lockers and is served by a transportation management organization; and the company has corporate vehicles available for work-related trips. Since these favorable conditions were already in place, the combination of the transit incentive and parking cost increase changed employees’ commuting habits.

Managing transportation during highway reconstruction: A recommended process

Major highway reconstruction can cause significant disruptions to existing travel patterns and economic activity. Reducing these impacts on travelers, shippers, businesses and residents requires that innovative and effective transportation management actions be developed and implemented. This paper reports the major findings and recommendations of a research study on managing transportation during highway, reconstruction. The primary objectives of the study were to investigate and document the critical interrelationships among state-of-the-art reconstruction and scheduling techniques, traffic accommodation strategies, construction quality control measures, and project planning and evaluation processes, and to formulate and recommend a corridor transportation management process that can be used to develop, implement and evaluate a transportation management plan of strategies to mitigate the corridor-wide impacts of major highway reconstruction.