Efficient Transport Planning Research Articles

Real-time prediction of transit origin–destination flows during underground incidents

Efficient transportation planning and management are critical for ensuring the smooth operation of rail transit systems, particularly in urban areas with high passenger demand. Real-time prediction of transit flow during underground incidents is vital for ensuring passenger safety, minimizing disruptions, and optimizing resource allocation. This study presents a comprehensive framework to address the considerable challenges associated with short-term origin–destination (OD) flow prediction, particularly during incidents. We propose integrating convolutional long short-term memory cells with self-attention cells to learn the long-range spatiotemporal information of historical OD flows. To overcome the absence of a real-time OD matrix, we introduce an improved graph convolution operation, considering the local connection of ridership, to capture vital spatiotemporal interaction patterns. Quantitative and qualitative analyses were conducted to evaluate the performance of the framework, providing insights into the role of each module. Experimental results suggest that incorporating incident matrix theory enhances the accuracy of capturing declining OD flows during incidents by up to approximately 25%. Furthermore, integrating external features like weather and time comprehensively assesses the impact of erratic change factors, especially during off-peak periods. Additionally, introducing more than one cycle of historical OD flow as an input may introduce noise, and increased attention is needed for OD flows with average daily flows exceeding 30 passengers. Designed with a high degree of modularity and adaptability, the proposed framework serves as a valid tool for transportation professionals and researchers to address evolving challenges in the management and operation of urban transportation systems.

Physics-informed deep learning with Kalman filter mixture for traffic state prediction

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model called the Physics Informed-Graph Convolutional Gated Recurrent Neural Network (PI-GRNN). Functioning as the state-space model within the KF, the PI-GRNN exploits established correlations to construct dynamic adjacency matrices that utilize the inherent structure and relationships within the transportation network to capture sequential patterns and dependencies over time. Furthermore, our methodology integrates insights gained from correlations into the correction step of the KF algorithm that helps in enhancing its correctional capabilities. This integrated approach proves instrumental in alleviating the inherent model drift associated with data-driven methods, as periodic corrections through update step of KF refine the predictions generated by PI-GRNN. To the best of our knowledge, this study represents a pioneering effort in integrating deep learning and KF algorithms in this unique symbiotic manner. Through extensive experimentation with real-world traffic data, we demonstrate the superior performance of our model compared to the benchmark approaches.

Urban delineation through a prism of intraday commute patterns.

Urban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies. We developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city. Our analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city. The study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.

From COVID-19 to the war in Ukraine: evidence of a Schumpeterian transformation of food logistics

This study analyzes the changes that have occurred in food logistics in the three years since the emergence of the COVID-19 pandemic and the one year since the war in Ukraine commenced. Food logistics companies are highly sensitive to demand shocks, energy prices, and staff availability. In this study, “first-hand” information was collected in the Iberian Peninsula, and it showed a process of Schumpeterian transformation. This crisis environment in which food logistics companies have been operating has opened a unique opportunity to renew operating procedures and seek new solutions, products, and markets. Therefore, food logistics companies have developed more effective communication strategies and innovative, profitable, and forward-looking commercial strategies to adapt to the new needs of their clients, applied more efficient transport planning and management methods, implemented new technologies to increase automation and digitization in warehouses, transport platforms, and trucks, and boosted market concentration and investment in infrastructure. Therefore, public authorities and top executives must focus on promoting and facilitating these improvements.

Computational Architecture of an Integrated Urban Model Considering Physical-Virtual Activity Spaces

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households’ and individuals’ long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model’s flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond.

Dynamic origin-destination matrix estimation for networks operating under free-flow conditions using macroscopic flow dynamics

The origin-destination (OD) matrix is a crucial requirement for efficient transportation planning, however its estimation still remains one of the most challenging tasks in transportation. This paper proposes a novel methodology for the estimation of dynamic OD matrices. The proposed methodology integrates disaggregated measurements from stationary sensors, e.g. loop detectors, with a macroscopic model that associates OD demands with Traffic counts on specific network links. We investigate networks that operate under free-flow conditions, hence the dynamic OD matrix estimation problem is formulated as a linear mathematical program. Finally, we reformulate the problem to obtain a computationally cheap quadratic programming problem that allows real-time estimation of OD matrices within the time-window under study. We demonstrate the effectiveness of our methodology through a literature network, showcasing its ability to accurately capture travel patterns in a computationally efficient manner, allowing informed decision-making processes in transportation planning and management.

A global empirical study on how street networks facilitate driving longer distances

We simulated over 200 cities worldwide to investigate how the street network affects vehicle routes. We demonstrate that there is a ubiquitous super-linear relationship between time and distance when optimal route are chosen. More precisely, the average speed will be higher for longer trips when compared to shorter trips, showing that the street network makes driving further faster. We attribute this phenomenon to the spatial arrangement of extensive street segments that eliminate deceleration points. These results underscore the importance for cities to consider the distribution of deceleration-free streets while mitigating any negative impact on sustainability. To ensure efficient transportation planning and engineering, innovative approaches are necessary to facilitate the flow of goods and services while adhering to sustainable mobility principles.

Transportation planning using activity-based travel demand model

According to Bangalore's Transportation Department, the number of transport vehicles in the city will be close to 85.6 lakh two wheelers, 50 lakh automobiles, and 20 lakh transport vehicles by the end of 2023. Two-wheelers account for 58.41% of all vehicles, while Light Motor Vehicles (LMVs) account for 22%, Heavy Motor Vehicle (HMVs) for 11%, and other vehicles account for 8.59%. Therefore, there is a need for efficient transportation planning. From earlier research, it was found that activity-based modeling is more efficient for evaluation. In this study, a method was proposed to develop an activity-based travel demand model for the selected zones of Bangalore city, and work trips were generated from the Global Tech Park. The trips were monitored, and it was found that people came from all around Bengaluru. Four zones in Bengaluru were chosen for this study because they provided the most travel to the Global Tech Park. The four zones were Kengeri, Mylasandra, RR Nagar, and Vijaynagar. The data were collected through individual person surveys, considering the influential parameters in developing person tours. The collected data were analyzed using SPSS software, and models were developed based on parameters such as age, sex, monthly income, distance traveled, active travel, daily travel cost, and vehicle ownership. The results obtained in the form of the models were compared with those of traditional models. In addition, the factors influencing the trips of each individual were studied, and the effects of these factors were analyzed. The results obtained were satisfactory in terms of the R2 value and other testing parameters. From the primary data analysis, it was found that the preference for modal choices of the public increased from public transport (i.e., buses) to private transport (i.e., owned cars) as per the increase in age, income, professions, travel distance, and daily travel cost.

Variations in Mode Choice of Residents Prior and during COVID-19: An Empirical Evidence from Johannesburg, South Africa

There have been numerous studies on the impact of COVID-19 on mobility in most developed countries; however, few of the studies have focused on the impact of the pandemic in developing countries, especially in Africa. In view of this, our study examined the impact of the pandemic on residents’ transportation mode choice in South Africa. This study adopted the use of both primary and secondary data obtained from TomTom statistics and an online survey of respondents’ mobility patterns before and during the pandemic. The questionnaire was administered through emails, and respondents were asked to provide information about their socio-economic characteristics, travel characteristics (before and during COVID-19), and the effect of COVID-19 on their travel patterns. A multinomial logistic model was adopted for analysis, and the findings revealed that variations existed in trip frequency, trip purpose, and mode choice of people before and during the pandemic. It was also discovered that respondents shifted from the use of public transport to private cars during the pandemic as a result of the implications for their health. Based on this, we propose that an enabling environment and an efficient transport planning technique should be adopted by the government and relevant stakeholders in the transport sector. This will integrate all modes of transport to reduce the over-reliance on private automobiles and also to encourage the use of non-motorized transport (walk/cycle) for sustainable transport planning in the future.

A Review on Problems Faced Due to Poor Transportation Facilities in Small Urban Cities in India

The development of any city majorly depends on efficient transportation planning. There are many different problems faced by small urban cities due to which the public is facing lots of inconvenience like public transport, culvert overflow, traffic congestion, lack of traffic signals etc. In most of the small urban cities in India, there are very few public transports available in the city due to this people use their own vehicle which increases traffic congestion. This also affects the environment by increasing noise and air pollution. Lack of traffic signals leads to improper flow of vehicles in the city which results in more chances of road accidents. In the rainy season from low height culverts, water overflows by that pedestrian as well as traffic flow gets affected. This is the review paper of various researches done on problems faced by small Indian cities and this will help us to suggest how it can be minimized.

ASSESSMENT OF VEHICULAR TRAFFIC CONGESTION IN YENAGOA, NIGERIA

he aim of this paper is to assess vehicular traffic congestion in Yenagoa, Bayelsa State, Nigeria. Traffic congestion is akin to major cities of developed and developing countries, the bane of urban life. The gravity model provided the theoretical foundation for this study, while a survey design involving volumetric count of vehicles and administration of questionnaire to 400 road users along major roads in Yenagoa was also used. The immediate causes of vehicular traffic congestion along major roads and conflict points in Yenagoa are: poor state of the roads, road side trading/market, inadequate public mass transit vehicles, road capacity, and poor traffic signalization. Generally, the effects of vehicular traffic congestion on road users include road traffic crashes and accidents, exposure of commuters and pedestrians to ambient pollutants with attendant health implications, parking problems, effects on aesthetics of buildings and the environment and encouragement of street trading. The result of the regression model revealed that the extracted components explained 0.614 (61.4%) of influence on vehicular traffic congestion in Yenagoa at 0.05 significance level. This study recommends an efficient transport planning system, the pull and push measures of traffic management such as introduction of mass transit and encouragement of off-street parking at affordable price

Estimation of Value of Time for a Congested Network – A Case Study of the National Highway, Karachi

Traffic congestion in mega cities is a common phenomenon for developing countries. Numerous studies on congestion cost estimation, that aim to quantify their monetary losses, have been conducted. Value of Time (VOT) assessment through utility maximizing theory and choice models are abundantly applied in transport literature. However, estimating VOT on congested network is not widely applied yet. To recognize the difference under normal and congested network, the current study focuses on VOT estimation for work trips in an extremely congested network.The focus of this research is to conduct a VOT estimation of the National Highway, Karachi. It connects Karachi city with Port Qasim Industrial area and the rest of the country. A large amount of freight transport to and from the port is also observed on this road. The National highway, being the only link to commute to this industrial area, is therefore under excessive traffic congestion.A stated preference (SP) survey was conducted at various industries located in this stretch. The respondents were asked about current travel practices and their (stated) preferences based on hypothetical -though realistic- travel attributes. A choice set of four alternative modes based on the currently used mode was presented to each individual. A Multinomial Logistics Regression (MNL) Model was developed for data analysis.As perceived, the results revealed a strong impact of travel time and travel cost on the (dis)utility of travel. These results can be utilized by policy makers to reduce congestion, monetary and time losses through efficient transport planning.

Accessibility, congestion and travel delays in Dar es Salaam – A time–distance perspective

This paper addresses the current state of the transport infrastructure in the city of Dar es Salaam providing both a methodological, a planning and a spatial accessibility perspective. Initially, a short background section describes the growth and spatial transformation of the city area based on spatially disaggregated data from the most recent inter-census period from 2002 to 2012. This period is characterized by extensive growth in terms of area, population and car use, while the road network largely maintains status quo except for ad-hoc development in new urban areas. The paper moves on to present a review of research into travel speed levels and congestion in Dar es Salaam. A set of city-wide maps of accessibility and delay levels are constructed based on available speed data and road network data obtained from the OpenStreetMap project and the findings are discussed with respect to the need for more efficient transport planning and implementation of plans.

Florida’s Efficient Transportation Decision-Making Process: Laying the Technology Foundation

In response to “environmental streamlining” legislation passed by the U.S. Congress as part of the Transportation Equity Act for the 21st Century, Florida has undertaken efforts to implement more efficient transportation planning and environmental review. These efforts have led to the development of the Efficient Transportation Decision-Making Process (ETDM Process), which redefines how Florida will accomplish planning and project development. A rather unique aspect of Florida’s streamlining approach is the integration of information technology as a vital foundation for the process. The development of Florida’s ETDM Process is described and evaluated, focusing on the information technology component. This component was developed as an interactive Internet-accessible geographic information system database. It integrates resource and project data from multiple sources into one standard format, provides quick and standardized analysis of the effects of the proposed projects on the human and natural environment, and supports the effective communication of results among all stakeholders, including the public. The use of technology is expected to reduce the cost of agency participation in the process and produce better, timely transportation decisions that reflect the proper balance among land use, mobility, and environment. Main topics include application design and development methodology, its integration in the ETDM Process, and how it has been received by the user community to date. Its benefits are evaluated, and recommendations for developing integrated technologies in support of streamlining efforts are provided.