Urban Transportation Planning Research Articles

Urban Road Transport Infrastructure Planning and Development Implications on Socio-Economic Growth in the City of Douala, Cameroon

Cities are the major contributors to socio- economic growth with easy access to amenities which stem through road transport infrastructure development. This study was conducted to assess urban road transport infrastructure planning and examine development implications on socio-economic growth in the City of Douala. A descriptive research design was used with primary and secondary data collected and analyzed using descriptive and inferential statistical techniques. Results revealed that, transport infrastructure in Douala have been structured through urban development planning policy which influences surface spatial expansion of socio- economic activities. The planning policies have contributed to the rapid expansion and growth through the initiations of master plan, land use plan and sector plan. Infrastructural development on road transport depicts a physical expansion and construction of road networks which influences the socio-economic growth of the study area. Stakeholders’ actions on construction, reconstruction and rehabilitation, investments and maintenance of road infrastructure have helped to curb down some transportation challenges and creates easy access to facilities within different sectors of the study area. However, road transport infrastructure which is well-developed boasts socio-economic activities and growth. The state of road transport network, its diversity and characteristic features is defined by its prevalence within different areas in Douala. Contrarily, other transport infrastructures which are not functioning appropriately due to lapses in physical planning possess enormous implications on transportation in the study area. The conclusion drawn was that the manner in which urban planning policies are implemented determines the level of development in road transport infrastructure and socio-economic growth of Douala. The study recommends an effective application of planning policies to be integrated by implicating all the actors of urban managers and transportation planners for improved growth in the study area.

An Integrated Framework for Estimating Origins and Destinations of Multimodal Multi-Commodity Import and Export Flows Using Multisource Data

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling.

Polycentricity and TOD-ness synergy: A novel composite index for integrated development of employment centers and residential cores in transit-oriented neighborhoods

Proximity of transit-oriented development (TOD) goals to polycentric urban development in the field of controlling negative effects of urban sprawl reinforces polycentricity and TOD-ness spatial synergy. Furthermore, identification of transit-oriented neighborhoods that have high potential to become secondary employment centers (ECs) is one of the most important challenges facing urban transport planners and land use policy makers. This research explained integrated development of ECs and residential cores (RCs) in transit-oriented neighborhoods using a novel composite index. The index is designed based on TOD-ness and polycentricity measures using spatial statistical methods and helps urban planners and policy makers to better identify polycentric TODs as transit-oriented residential neighborhoods with high potential to become secondary employment centers. Research findings confirmed higher accessibility of polycentric TODs to public services and public and private transportation that have better integration in the development of urban facilities and transportation systems. Furthermore, the findings confirmed polycentricity and TOD-ness spatial synergies in polycentric TODs.

Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing

Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility.

Αn integrated framework to monitor progress of sustainable urban development strategies, addressing societal, ecological and innovation dilemmas

This paper presents an integrated Monitoring and Evaluation (M&E) framework, an all-encompassing approach suitable for European cities and regions aiming to monitor and evaluate the implementation and results of sustainable urban development strategies or projects related to urban planning, transport planning, or urban energy management. The framework features a co-created and co-implemented reflexive and adaptive method tailored specifically for strategies or projects that introduce innovative elements from contemporary research and innovation in science and technology, such as net-zero development, systemic solutions for cities, or integrated urban mobility and planning. The approach to designing and implementing the M&E framework was realized through a six-phase process: (i) development of the M&E framework, (ii) tailoring indicators to each region’s specificities, (iii) developing data collection questionnaires and a reporting database, (iv) collecting the data, (v) populating the database, and (vi) writing the final report. The framework was tested and enhanced with four European regional authorities over a 19-month period, enabling them to address their so-called 'policy dilemmas,' defined as the challenges they face in balancing societal and inclusivity objectives with economic and innovation goals. This framework is designed for replication in future sustainable urban development strategies or projects with similar societal, ecological, and innovation objectives.

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

Travel-mode inference based on GPS-trajectory data through multi-scale mixed attention mechanism

Identifying travel modes is essential for modern urban transportation planning and management. Recent advancements in data collection, especially those involving Global Positioning System (GPS) technology, offer promising opportunities for rapidly and accurately inferring users' travel modes. This study presents an innovative method for inferring travel modes from GPS trajectory data. The method utilizes multi-scale convolutional techniques to capture and analyze both temporal and spatial information of the data, thereby revealing the underlying spatiotemporal relationships inherent in user movement and behavior patterns. In addition, an attention mechanism is integrated into the model to enable autonomous learning. This mechanism enhances the model's capacity to identify and emphasize key information across different time periods and spatial locations, thus improving the accuracy of travel mode inference. Evaluation on the open-source GPS trajectory dataset, GeoLife, demonstrates that the proposed method attained an accuracy of 83.3%. This result highlights the effectiveness of the method, demonstrating that the model can more accurately understand and predict user travel modes through the integration of multi-scale convolutional technologies and attention mechanisms.

Proposing an ethics of care: Tracing Victoria's transport planning history

AbstractThis paper examines the question what kind of ethics prevail in contemporary urban transport planning and what potential does an ethics of care hold for practice? Researchers have given ample attention to the need for better governance and coordination, and despite acknowledging the need to reduce reliance on private cars, little has been said by them about what ethics can or should guide planning to bring about such frameworks for caring. This area of research merits urgent work given our collective need to address the socio‐spatial, climate, and health impacts of car dependence. Taking as our focus transport planning in Victoria, Australia, we consider how an ethics of care could open new ways to redress how transport planning has perpetuated injustices in metropolitan Melbourne. We draw on secondary research to consider both the conditions that cultivated the current transport planning landscape and pathways for possible change that lie ahead. The research highlights opportunities to consider care as an ethical framework for transport planning that could amplify justice and equity claims in urban transport planning for Australian cities and that has salience for other cities elsewhere.

An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxis

ABSTRACT A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy for a given zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips’ destinations. Likewise, the destination-entropy for a given zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips’ origins. Furthermore, we have created an interactive geovisualization that enables researchers to delve into and juxtapose the spatial and temporal dynamics of origin and destination entropy, in conjunction with trip counts for both origins and destinations. Results indicate that entropy-based measurements effectively capture shifts in the diversity of trips’ geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic. These measurements, alongside trip counts, offer a more comprehensive understanding of urban human flows.

Unpacking the perceived cycling safety of road environment using street view imagery and cycle accident data

Cycling, as a routine mode of travel, offers significant benefits in promoting health, eliminating emissions, and alleviating traffic congestion. Many cities, including London, have introduced various policies and measures to promote ’active travel’ in view of its manifold advantages. Nevertheless, the reality is not as desirable as expected. Existing studies suggest that cyclists’ perceptions of cycling safety significantly hinder the broader adoption of cycling. Our study investigates the perceived cycling safety and unpacks the association between the cycling safety level and the road environment, taking London as a case study. First, we proposed novel cycling safety level indicators that incorporate both collision and injury risks, based on which a tri-tiered cycling safety level prediction spanning the entirety of London’s road network has been generated with good accuracy. Second, we assessed the road environment by harnessing imagery features of street view reflecting the cyclist’s perception of space and combined it with road features of cycle accident sites. Finally, associations between road environment features and cycling safety levels have been explained using SHAP values, leading to tailored policy recommendations. Our research has identified several key factors that contribute to a risky environment for cycling. Among these, the “second road effects,” which refers to roads intersecting with the road where the accident occurred, is the most critical to cycling safety levels. This would also support and further contribute to the literature on road safety. Other results related to road greenery, speed limits, etc, are also discussed in detail. In summary, our study offers insights into urban design and transport planning, emphasising the perceived cycling safety of road environment.

Urban Road Surface Condition Sensing from Crowd-Sourced Trajectories Based on the Detecting and Clustering Framework.

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users' travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications.

Embracing Urban Micromobility: A Comparative Study of E-Scooter Adoption in Washington, D.C., Miami, and Los Angeles

E-scooters have emerged as a popular micromobility option for short trips, with many cities embracing shared e-scooters to enhance convenience for travelers and reduce reliance on automobiles. Despite their rising popularity, there is a lack of clear understanding of how user preferences and adoption practices vary by location. This study aims to explore user and non-user attitudes towards e-scooter use in diverse urban settings. A meta-analysis of data from three surveys (N = 1197) conducted in Washington, D.C., Miami, FL, and Los Angeles, CA, was performed to compare e-scooter users and non-user profiles, mode choice factors, and attitudes and preferences towards e-scooter use. Additionally, machine learning (ML) and SHAP (SHapley Additive exPlanations) analysis were utilized to identify influential factors in predicting e-scooter use in each city. The results reveal that the majority of e-scooter users are 25 to 39 of age, male, with higher income and a bachelor’s degree, and 92% possess a driver’s license. Significant differences in attitudes between e-scooter users and non-users highlight the complexity of perceptions towards e-scooter usage. The ML model indicates that employment status negatively impacts the prediction of e-scooter users, while factors such as living without a car and using non-motorized modes positively influence e-scooter use. Educational background is a significant e-scooter mode choice factor in Washington, D.C. and Miami, whereas attitudinal questions on car and technology usage are influential in Los Angeles. These findings provide valuable insights into the factors shaping e-scooter adoption, informing urban transportation planning and policymaking and enhancing understanding of shared micromobility and its impact on urban mobility.

Empowering Urban Public Transport Planning Process for Medium-Sized Cities in Developing Countries: Innovative Decision Support Framework for Sustainability

The challenges resulting from rapid economic growth, urbanization, and increased motorization in developing nations necessitate a comprehensive and sustainable approach to urban public transport planning. While sustainable urban public transport (SUPT) planning offers a solution, the complexity of choosing suitable policy measure options remains a challenge. This study first introduces a decision support framework (DSF) that integrates the sustainable urban public transport manual (SUPTM) adopted for generating the potential SUPT policy measure options, the KonSULT knowledge base applied for providing the performance scores of each measure option for all determined criteria, and the HMADM (including FAHP, FSM, and TOPSIS) technique to create, rank, and select SUPT policy measure options tailored to medium-sized urban areas in developing nations. A case study of Khon Kaen City, Thailand, illustrates the practical application of the framework, resulting in a set of 31 (91.2%) out of the total of 34 ranked policy measure options. Comparing these prioritizations with the city’s existing plan reveals a substantial agreement, which suggests the potential applicability of the DSF. Overall, the DSF marks a significant advancement in SUPT planning, which is crucial for shaping efficient, equitable, and environmentally conscious urban mobility in developing countries, which are undergoing transformative change.

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

Emission calibrated traffic model for environmental-sensitive decision making in Sri Lanka: Review

Air pollution from road transportation is a growing concern in urban areas, particularly in Sri Lanka, where vehicle emissions account for a significant portion of air pollutants. The increase in the number of vehicles on the road and the resulting traffic congestion exacerbate this problem. Therefore, accurate prediction of emissions and air quality related to road traffic is essential for informed decision-making in traffic planning and management. While Sri Lanka has made progress in developing driving cycles and traffic flow simulations tailored to its unique context, there remains a gap in calibrating these models with local emission values, which are influenced by specific driver behaviours and operational modes. An emission-calibrated traffic model specific to the Sri Lankan context can integrate both traffic-related and environmental factors, providing a comprehensive tool for analyzing the impact of traffic management strategies on emissions and air quality. By addressing the discrepancies between global manufacturer data and local conditions, this model can enable more environmentally friendly decisions in urban transportation planning, contributing to the reduction of air pollution. The study identifies, through a comprehensive review, the need for an emission-calibrated traffic model to address the unique challenges posed by Sri Lanka's evolving traffic landscape. While traffic modelling has made strides in Sri Lanka, this study identifies a significant research gap in modelling the emissions generated by these traffic models, especially considering the unique characteristics of the region's roads and vehicles. Further, the paper concludes that an emission-calibrated traffic model specifically tailored to the Sri Lankan context is essential to guide environmentally friendly traffic planning decisions and bridge the gap between traffic data and its environmental consequences.

Causal inference of Seoul bike sharing demand

The global surge in environmental consciousness has significantly boosted the demand for rental bikes, particularly in metropolitan areas such as Seoul. This study delves into the causal relationships affecting this demand using a dataset from Seoul’s bike-sharing system. Unlike previous research focusing predominantly on predictive analytics, this work innovatively applies multiple linear regression models to uncover causal inferences, offering insights that extend beyond mere forecasting. The challenges addressed include dealing with non-linear relationships and heteroscedasticity by employing the logarithmic transformation of rental counts. This approach not only aids in normalizing the data but also enhances the interpretability of the regression outcomes, emphasizing the changes in demand as a function of various environmental and temporal variables. Recent developments in causal inference methodologies have allowed for more robust and detailed analysis, paving the way for this study’s contribution to the field. The findings underscore the significant influence of factors such as hour of the day, humidity, and seasonal changes on bike rental volumes, which can inform policy-making and operational strategies in urban transport planning.

Security Analysis of Pedestrian Comfort on the Use of Pedestrian Paths in the Bung Hatta Palace Area

Many pedestrian paths are still not well organized and optimally utilized. On the other hand, the comfort of pedestrian paths should be a priority in urban transportation planning. This study aims to determine the level of pedestrian service on Jl. Istana and Jl. H. Agus Salim Bukittinggi city and to determine the perceptions of people who use pedestrian crossing facilities on Jl. Istana and Jl. H. Agus Salim in the Bung Hatta Palace area Bukittinggi based on comfort and level of pedestrian service. This research uses descriptive research methods carried out to explain the situation that occurs on the pedestrian path Jl. Istana and Jl. H. Agus Salim in the Bung Hatta Palace area Bukittinggi. For the results of pedestrian movement characteristics obtained data Flow (Flow) in segment I amounted to 0.12, 0.70 and 0.75 pedestrians / min, in segment II amounted to 0.29, 1.13 and 1.67 pedestrians / min. Speed data in segment I was found to be 52.32, 55.97 and 54.97 m/min, in segment II it was 55.03, 51.74 and 53.96 m/min. Density in segment I was 0.18, 0.19 and 0.18 m2/ped, in segment II it was 0.62, 0.57 and 0.60 m2/ped. Space in segment I was 5.65, 5.38 and 5.50 m2/ped, segment II was 1.62, 1.74 and 1.67 m2/ped. Pedestrian ratio in segment I amounted to 0.0016, 0.0094 and 0.0100, segment II amounted to 0.00380, 0.01509 and 0.02227. The results showed that the level of pedestrian service on Jl. Istana and Jl. H. Agus Salim in the Bung Hatta Palace area Bukittinggi is A, because pedestrians can move freely according to their wishes without any interference from other pedestrians and for the results of the questionnaire that has been distributed as many as 100 respondents obtained a comfort value of 72% and this figure is included in the comfortable category.

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

Exploring the Landscape of Autonomous Vehicles Research: A Scientometric Analysis in the Context of Urban Transportation Planning

Exploring the Landscape of Autonomous Vehicles Research: A Scientometric Analysis in the Context of Urban Transportation Planning

Traffic modeling and accidental data analysis using GIS: A Review

Nowadays, congestion and accidents are creating major risks to cities, including delays, higher fuel usage, and compromised safety. Effective traffic modelling and accident data analysis are critical for identifying high risk identifying accident-prone locations, understanding the causes of accidents and creating focused actions to enhance traffic flow and safety. GIS is an effective tool for integrating, analysing and visualizing different geographical data relevant to transportation networks such as, traffic flow, infrastructure, and safety. It enables geographical analysis and visualization of accident hotspots by integrating accident data, road conditions, traffic numbers, and environmental factors. The use of GIS in traffic modelling and accident data analysis provides considerable benefits in urban transportation planning and management. The aim of the paper is to provide an overview of the application of GIS in traffic modelling and accidental data analysis, highlighting the methodologies, advancements, and challenges in this field. The review shall provide a comprehensive assessment of the current state of traffic modelling and accidental data analysis using GIS. It will highlight the significant contributions of GIS technology, identify key research gaps, and offer insights into future directions for enhancing transportation planning and decision-making processes.