Urban Transport Modes Research Articles

Modeling of an Urban Ropeway Integrated into a Crowded Transit System

In many moderately sized European cities, the public transport systems based on trams and buses are operating at their capacity limits. Ropeways have proved to be a suitable transit extension in several Latin American cities. Few travel demand models attempt to forecast the impact of urban ropeways. So far, all of these models do not consider the specific properties of a ropeway. This paper seeks to estimate a mode choice model that includes a ropeway as a separate transport system. Relative to bus operation in European cities, ropeways promise improved timetable keeping, with fewer delays at the start because of high service rates, and they also offer improved passenger comfort with higher capacities. These benefits must be reflected in the travel demand model by mode-specific parameter settings that are estimated based on a survey. A stated choice experiment was conducted, in which respondents compared realistic trip situations using a ropeway with traditional urban transport modes, with aspects including access and egress time, waiting time, travel time, travel costs, reliability, and crowding. The situations of choice were selected from observed trip data to be as realistic as possible. Using a mixed logit (ML) model, the parameter estimation indicates that crowding and reliability as well as the personal attitude of potential users have a statistically significant influence on the choice behavior of people in Graz, a moderately sized city in Austria.

Основы методики выбора и обоснования видов городского общественного транспорта Ирака

Purpose: Improving the urban public transport (UPT) system to ensure its efficient operation in Iraqi cities. Choice and justification of the urban transport mode in Najaf (Iraq), which will improve safety and speed of movement of passengers. Justification of the application areas for different types of transport in Iraqi cities by optimizing and selecting modes of passenger transport based on traffic volume, passenger density and urban characteristics. Methods: Statistical processing, functional analysis, mathematical modelling, probability theory, analysis of urban passenger transport in Iraqi cities, classifying Iraqi cities by main characteristics: population density, area, level of motorization, the maximum length of the territory, analysis of passenger traffic intensity and average capacity of the rolling stock. Results: New boundaries of average passenger flow density intervals have been adjusted for groups of Iraqi cities, allowing for the selection of the type of UPT and its share of transportation for each type of rolling stock. A distribution of urban transport modes has been proposed, taking into account the characteristics of Iraqi cities according to their classification. Practical significance: The application of the developed proposals and activities allows: to justify and select the types of urban transport systems for the city of Najaf; to calculate passenger traffic on six different routes with maximum traffic for the city of Najaf; to determine the classification of rolling stock capacity series and the UPT options for Iraqi cities; to provide recommendations for the development of the transport network of Iraqi cities. The proposed distribution of urban transport modes improves public transport services and the quality of use of each mode of transport.

Macroscopic Fundamental Diagrams for Low-Altitude Air city Transport

Low-altitude aircraft is being developed as a new mode of urban transport; consequently, the penetration of low-altitude aircraft into the urban airspace is inevitable in the near future. This will give rise to new urban air transport systems, called low-altitude air city transport (LAAT) systems. Such systems will include aircraft operated with or without pilots, transferring passengers and goods in urban areas using low-altitude levels of urban airspace.This paper investigates the collective and aggregate aircraft traffic flow diagrams, i.e. Macroscopic Fundamental Diagrams (MFDs), for LAAT systems. A LAAT plant model that captures the microscopic and macroscopic levels is established to construct the MFD curve, which links flow, density, and speed. The MFD allows us to identify the airspace traffic conditions, and to detect congested regimes. Different case study examples were simulated to analyze the full shape of MFD curves for LAAT systems, considering different algorithms (waypoints and destination plane), aircraft, and airspace settings. In this paper, a unique modeling framework for LAAT operation and a real-time synchronized implementation of large-scale LAAT simulation were developed. The findings of the current paper, the development of LAAT simulation environment, and the identification of airspace traffic conditions, can lead to the development of aggregate models and new control strategies to minimize congestion in futuristic urban airspace.

Urban transport, logistics, and tourism: Review of a cutting-edge socially-oriented approach to industrial development

Motives: Rapid urbanization has increased public awareness about logistics support for urban traffic flows. Given this interest, the relevance of this research is beyond any doubt. Aim: The study aims to identify the most cutting-edge approaches for facilitating traffic and tourism flows using various urban transport modes, and to define the opportunities for their application on the domestic market. Results: The results of the conducted analyses suggest that the development of rail transport is the most promising and vital step in improving urban transport and logistics infrastructure, including in the tourism sector. The study established that intelligent transport systems in urban areas, including public transport, logistics flows, tourist flows, parking, and road traffic control, are complex management systems. A critical examination of several cases supported the identification of regularities in factors that affect the dynamics of urban traffic flows. In view of the rapid development of transport and logistics infrastructure around the world, development opportunities in rural areas bordering large cities were identified. The most effective approaches to resolving logistics problems in a period of war were determined.

Impact of COVID-19 pandemic on the people’s choice of urban public transportation modes and mobility in Addis Ababa and Hawassa city, Ethiopia

ABSTRACT The aim of this study is to investigate the impacts of COVID-19 pandemic on public transportation mode choices in Ethiopia. Protection motivation theory and multinomial regression model were applied to estimate the mode choice behavior during the pandemic, compared to the pre-COVID period. Results indicated that public transport modes are the dominant modes before and during the pandemic but the choice of these modes declined during the pandemic by about 33 percent. Respondents placed more priority on pandemic-related risk, cleanliness, social-physical distancing, good airflow, and travel cost when deciding mode choice during the pandemic, compared to the pre-COVID period. Pandemic-related factors, gender, income, distance of residences, and travel cost are the significant predictors of mode choice. Passengers are also likely to choose a lesser amount of public transportation whereas more choice for alternative modes. The reasons are the passengers’ perception of travel risks, greater exposure to infections; shortage of infection prevention services; unaffordable service fees; disruption of services, being made out of work and working from home. Overall, the study could inform that COVID-19 pandemic is really impacting the mode choice behaviors of urban people. It will imply policymaking to prepare pandemic-sensitive and adaptive transport systems during & post-pandemic period.

Modeling interactions between the Montreal subway and other urban transportation modes at the station level

Subway systems are an example of how underground space can contribute to developing more sustainable cities. Their use can be encouraged by integrating other urban transportation modes to provide easy access to the stations, thus facilitating intermodal connectivity. However, few studies have addressed data and modeling issues with multiple modes. This gap, mainly due to the lack of appropriate methods, explains why relationships between traditional and emerging modes remain poorly understood; there is still debate about whether they are complementary or competitive. This paper investigates this question by examining interactions between three alternative modes (bikesharing, free-floating carsharing and taxi) and the Montreal (Quebec, Canada) subway over two months. To this end, several operational passive datasets are merged. A two-level growth model, including both fixed and random effects, is then developed based on time series of daily numbers of boardings at subway stations. This longitudinal model quantifies the effect of various variables (calendar, weather, socio-demographic and economic, network connectivity, land use and built environment attributes) on each station use. The correlations between subway boardings and alternative mode destinations are also measured to detect potential intermodal relations. Finally, a further categorical analysis is conducted by period and station type to specify these interactions in time and space. Results reveal that alternative modes tend to complement the subway in the network periphery (as possible access mode to the subway stations), but may compete with the subway in areas closer to the city center. Such findings could help policymakers determine the location of the mobility services to enhance first-mile subway access, as well as the best mode integration around subway stations in planning strategies.

Exploring Consumers’ Intention to Use Bikes and E-Scooters during the COVID-19 Pandemic in the Philippines: An Extended Theory of Planned Behavior Approach with a Consideration of Pro-Environmental Identity

The onset of the COVID-19 pandemic has prompted nations globally to adopt lockdown policies, resulting in a substantial shift in people’s travel behavior. This pandemic has influenced micro-mobility, an emerging urban transportation mode, including in the Philippines. However, a limited quantifiable study is dedicated to understanding the evolving micro-mobility use in response to the COVID-19 pandemic. This study aims to determine the impacts of the COVID-19 pandemic on our future intention to use micro-mobility, particularly e-scooters/bikes, and to explore how hedonic and environmental motivations influence the decision-making process of its adoption. An extended Theory of Planned Behavior (TPB) was employed, with 152 Filipinos answering an online questionnaire that was distributed using a convenience sampling approach. Structural Equation Modeling (SEM) showed that the COVID-19 pandemic emphasized the awareness of environmental issues and the negative impact of cars, thereby significantly influencing the usage intention of e-scooters/bikes. In addition, social norms and perceived behavioral control partially mediated the relationship between the COVID-19 pandemic and the intention to use micro-mobility. Meanwhile, the magnitude and significance of motivation variables were inconclusive, although they confirmed a positive relationship with attitude. The findings of this study could help provider firms and policymakers devise evidence-based policies concerning micro-mobility and promote its usage, particularly in developing countries.

Describing Micro-Mobility First/Last-Mile Routing Behavior in Urban Road Networks through a Novel Modeling Approach

E-scooters aspire to provide flexibility to their users while covering the first/last mile of a multimodal trip. Yet, their dual travel behavior, i.e., utilizing both vehicles’ roadways and pedestrians’ sidewalks, creates new challenges to transport modelers. This study aims to model e-scooter riding behavior in comparison to traditional urban transport modes, namely car and walking. The new modeling approach is based on perceived safety that is influenced by the road environment and affects routing behavior. An ordinal logistic model of perceived safety is applied to classify road links in a 7-point Likert scale. The parametric utility function combines only three basic parameters: time, cost, and perceived safety. First/last mile routing choices are modeled in a test road network developed in Athens, Greece, utilizing the shortest-path algorithm. The proposed modeling approach proved to be useful, as the road environment of an urban area is heterogenous in terms of safety perceptions. Indeed, the model outputs show that the flexibility of e-scooters is limited in practice by their low-perceived safety. To avoid unsafe road environments where motorized traffic dominates, e-scooter riders tend to detour. This decision-making process tool can identify road network discontinuities. Nevertheless, their significance regarding routing behavior should be further discussed.

Robustness assessments of urban rail transit networks based on user equilibrium with time compensation mechanism

Urban rail transit has become an important mode of urban public transportation and has been paid more attention in the past two decades due to the advantages of low pollution and high efficiency. However, traffic incidents frequently occur in urban rail transit, therefore the robustness must be investigated to improve operation safety. In this paper, the user equilibrium with time compensation mechanism is adopted to study the robustness of urban rail transit networks (URTNs) subjected to failures, and Shanghai metro network is considered as an example to prove the feasibility and effectiveness of the proposed scheme. Meanwhile, the average travel time, the average link-flow and the demand–supply ratio are used to assess the characteristic changes of URTNs subjected to three attack modes. Moreover, the critical nodes and links can be discovered based on the proposed model, and the attacks to these critical nodes and links can greatly decrease the robustness of URTNs. The simulation results show that URTNs have better robustness when suffering the node attack and the link-demand attack and possess worse robustness when suffering the overall-demand attack, and we find that the uneven distribution of passenger flow results in the low network utilization.

Exploring the Macro Economic and Transport Influencing Factors of Urban Public Transport Mode Share: A Bayesian Structural Equation Model Approach

The public transportation priority strategy is a significant way to alleviate urban traffic congestion, and the urban public transport mode share (UPTMS) is a crucial indicator to measure the performance of public transportation priority strategies. To explore the influence factors of UPTMS, this study hypothesized that UPTMS is influenced by factors such as population, economy, road operation performance, public transport infrastructure, and private transport facilities, and tested the hypotheses using structural equation modeling (SEM) and Bayesian structural equation modeling (BSEM) based on urban macro economic and transport data in Guangzhou and Beijing, China. The results showed that, in the case of a small sample, BSEM is more adept at examining the correlation between the UPTMS and its influencing factors than SEM; in the Guangzhou and Beijing models, public transport infrastructure has the greatest positive and most significant impact on the UPTMS, and road operation performance has the greatest negative and most significant impact. Moreover, road operation performance is significantly improved by public transport infrastructure, while private transport facilities have a significant negative influence on road operation performance.

Urban drinking and driving: comparison of electric scooter and bicycle related accidents in facial fracture patients.

In recent years, electric scooters (e-scooter) have emerged as an alternative mode of urban transport due to their availability and effortless use. However, e-scooter-related trauma and injuries, especially to the head, have received wide media coverage and raised public concern about their safety. We aim to determine and compare clinically relevant variables, incidence, and severity between bicycle and e-scooter-related facial fractures and potential protective measures for injury prevention. This retrospective study comprised all patients admitted to a tertiary trauma center with bicycle or e-scooter-related facial fractures between January 2019 and October 2020. Patient- and injury-related variables, including demographics, injury mechanisms, helmet use, influence of alcohol, types of facial injuries, types of other injuries, given treatment, and hospital stay, were collected, analysed, and compared between bicycle and e-scooter injuries. Altogether 169 patients with facial fractures, 124 bicycle-related injuries (73.4%) and 45 e-scooter-related injuries (26.6%) were included. Alcohol involvement was significantly higher in e-scooter patients (88.9%) than in bicycle patients (31.5%) (p<0.001). Driving under the influence of alcohol was associated with driving without a helmet in both groups (p<0.001). In multivariate analyses, e-scooter accidents were 18 times more likely to occur under the influence of alcohol (OR 17.85, p<0.001) and were more likely to involve collision with a stationary object (OR 3.81, p=0.028). E-scooter patients were significantly younger (OR 0.95, p<0.001) and had significantly more cranial fractures (OR 10.15, p=0.014) than bicycle patients. Compared with patients in bicycle accidents, facial fracture patients injured in e-scooter accidents are younger, are more likely under the influence of alcohol, and sustain more severe craniofacial skeleton fractures. Our results for both groups of patients advocate stricter adherence to helmet and road safety legislation as well as public education for injury prevention.

Linking integrity with road pricing cause-and-effect model: A system dynamics simulation approach

With Malaysia's rapid urbanisation and continuous improvement of living standards, vehicle ownership and trip volume continue to grow. Increases in motor traffic in large cities and their environs result in a number of social, environmental, and economic issues, which are frequently attributable to the widespread use of automobiles as the primary mode of urban transportation. This exacerbates traffic congestion on the country's highways, particularly in urban areas such as Kuala Lumpur. This traffic congestion poses an ongoing threat to the sustainability of transport development. Thus, by using the system dynamics, this study establishes a cause-and-effect relationship regarding the implementation of road pricing as a tool for reducing congestion and a stepping stone for enhancing sustainability. Road pricing is a direct charge assessed to drivers who use the road network with the goal of reducing the number of private vehicles on the road during peak hours. The developed Causal Loop Diagram (CLD) composed of five subsystems: road congestion, road attractiveness, new road construction, public transportation, and road pricing. The road congestion, new road construction, and road pricing all encounter mutual reinforcement as a result of a variety of negative polarities. As a result, authorities should place a greater emphasis on these loopholes, as they will inevitably result in unexpected changes. Additionally, by incorporating holistic perspectives from previous works and experts in the field, CLD can aid in identifying the primary factors underlying the problem being studied. In future work, the developed CLD should be extended to the next stage of the SD model, dubbed stock-flow-diagram (SFD).

Geo-fence planning for dockless bike-sharing systems: a GIS-based multi-criteria decision analysis framework

The inappropriate parking of free-floating shared bikes is a critical issue that needs to be addressed to realize the potential environmental, socioeconomic, and health benefits of this emerging green mode of transport. To address this challenge, this paper developes a Geographic Information Systems (GIS) based Multi-Criteria Decision Analysis (MCDA) framework for geo-fence planning of dockless bike-sharing systems based on openly accessible data. The Analytic Hierarchy Process (AHP) and the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method are applied in the proposed framework to derive optimal geo-fence locations. The proposed framework is validated in a case study using a dataset of dockless bike-sharing trips from February 2020 in the City of Zurich and comparing the selected geo-fence locations with the existing bike-sharing stations. The assessment results show that the calculated geo-fence locations have a smaller average distance of 1395 m than that of 1692 m, and a larger demand coverage of 81% than that of 77% for bike-sharing stations. Overall, the proposed framework and the insights from the case study can help transport planners better implement shared micro-mobility hence facilitating the uptake of this sustainable mode of urban transport.

Comparing Inequality in Future Urban Transport Modes by Doughnut Economy Concept

Inequality is a problem facing the world community, especially in developing countries, that affects urban transport and vice versa. Which possible urban transportation mode will cause the least inequality? This is a vital question. The development of Autonomous vehicles (AV) has made Shared Autonomous Vehicles (SAV) one of the future transport modes. Active and public transport are also mentioned as applicable future modes, based on the literature. This paper aims to compare inequality in active transportation, public transport and SAV as the most important alternatives to private cars in the future. In this regard, we use doughnut economic concepts as the framework for our comparison. First, the inequality concept is expanded and then literature demonstrates the future desirability of modes. We show why doughnut economics could be a beneficial alternative for comparing that resulted in the superiority of active and public transport over SAV in terms of future inequality.

Active transportation policy and practice in the city of Oulu from 1998 to 2016—A mixed methods study

Land use and transportation policies have been recognized globally as major sources of physical inactivity, but there has been a gap between research and policy implementation. Our objective for this research was to produce an integrated view of community planning policies and the association between urban form characteristics and transportation mode choices in the city of Oulu from 1998 to 2016. Our findings showed that increasing density and diversity of the urban form, emphasizing active transportation, and developing the city center were highlighted in the community and transportation planning policies. In practice, urban form development focused on the inner city, but in the outer urban area and urban fringe, sprawl and car dependency increased. Overall, the active transportation mode share decreased by 2 percentage points during the follow-up, but increases in density, mix and access networks were associated with increased walking and cycling compared to car use. In conclusion, no consensus was established in Oulu to limit the dominance of private motor vehicles. Decreased active transportation mode share might have been due to inadequately assessed functional mix outside the inner city, increased urban sprawl and building more capacity for cars. In the future, stronger political leadership, increased density, better access to nearby services combined with investments in public transportation will be required to meet the policy goals.

Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes

Given the increasing prevalence of new urban transport modes such as ridesharing, e-hailing, and combined transport, it is essential to evaluate their effects on the capacity of transportation networks. Hence, this paper develops a novel transportation network capacity model to capture the travel behaviors of inter-multimodal mobility in an urban transportation system that incorporates emerging travel modes. The novel model is formulated as a bi-level programming problem, in which the lower-level model is a combined modal split and traffic assignment (CMSTA) problem based on mathematical programming. The CMSTA problem adopts the cross-nested logit model to account for intermodal travel behavior in the modal split phase and the path-sized logit model to account for route overlap in the traffic assignment phase. Moreover, the logit-based trip distribution model is used to capture the dispatch of the e-hailing traffic flow and the matching of ridesharing drivers with passengers. Besides, we consider flow interactions (e.g., cars and buses sharing the same link) in the road network. We customize a solution framework for solving this novel model that adopts the recently developed fast path-based algorithm with the Barzilai–Borwein stepsize strategy to efficiently solve the CMSTA problem, and derive a sensitivity analysis-based (SAB) algorithm to solve the entire bi-level programming problem. The effectiveness of the novel model is verified in numerical experiments that demonstrate the effects of intermodal transportation, e-hailing, and ridesharing on the capacity of a multimodal transportation network.

Racial Differences and Pedestrian Safety: Some Evidence from Maryland and Implications for Policy

In the 1990s, federal legislation was passed requiring transportation planning to incorporate the interests of all stakeholder groups. One urban transportation mode that is often ignored is pedestrian movement. A group particularly susceptible to difficulties in this mode is the elderly. This study surveys the attitudes of senior citizens in regard to pedestrian safety and motorist interactions. We examine the attitudes of elderly pedestrians concerning many aspects of pedestrian travel. The study finds that this group often views itself as disenfranchised. This study provides insight into what areas of pedestrian activity the elderly find troublesome and looks at the differences between urban and rural attitudes in regard to this issue.The results show that African Americans and Asians have a much higher rate of positive (safety conscious) responses to the questions in the survey. This indicates that they believe that they are more aware of pedestrian safety and that they comply with laws regarding pedestrian safety at a higher rate than their Caucasian counterparts. In addition, gender and geographical location seem to impact the responses. Urban seniors have a much higher positive response rate than suburban and ex-urban seniors.A comparison with previous work finds that these attitudes expressed in the survey are at odds with the known demographic data concerning who is at higher risk to be involved in a pedestrian accident. The increased awareness on the part of urban respondents is compatible with the more pedestrian-friendly environment of urban areas, but the racial difference in responses is not compatible with the accident data.

Modeling Network Capacity for Urban Multimodal Transportation Applications

Since the diversity of urban transport modes and the growth of public transport demands recently, it is essential to consider the multiple mode options in the network capacity problem. This paper derives a comprehensive network capacity model from a single-mode transportation network with only route choice to a multimodal transportation network with both mode choice and route choice. To avoid biases in the evaluation of the multimodal network capacity, two characteristics of the multimodal transportation system are considered in modeling and formulating the problem: (1) the mode interaction between cars and buses is explicitly reflected when they share the same link; (2) the correlation of travel alternatives (modes or routes) is measured by developing a combined modal split and traffic assignment (CMSTA) problem, in which the nested logit (NL) model is employed to account for mode similarity in mode split, while the path-size logit model (PSL) is employed to account for route overlapping in traffic assignment. Numerical experiments demonstrate the characteristics of the new model. It also shows how planning schemes or management strategies affect the multimodal transportation network capacity via a real network case.

Multitask Hypergraph Convolutional Networks: A Heterogeneous Traffic Prediction Framework

Traffic prediction methods on a single-source data have achieved excellent results in recent years, especially the Graph Convolutional Networks (GCN) based models with spatio-temporal dependency. In reality, various modes of urban transportation operate simultaneously. They influence and complement each other in common space-time occasions, constituting the transportation system dynamically. Thus, traffic data from multiple sources is ostensibly heterogeneous, but internally correlated. The typical single data driven models are, however, not universally applicable for heterogeneous traffic data. To address this issue, we propose a Multi-task Hypergraph Convolutional Neural Network (MT-HGCN) for the multi-source traffic prediction problem. The framework consists of a main task and a related task. Both tasks are based on Hypergraph Convolutional Neural Networks (HGCN) and are devoted to two prediction problems. Furthermore, the tasks are bridged by a feature compress unit, which models the correlation and shares the latent feature to improve the performance of the main task. The node-level forecasting has been evaluated on historical datasets of Beijing to verify the effectiveness of the proposed method. Compared with the state-of-the-arts, the superior performance of the proposed method can be obtained.

Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction

Due to the increasing diversification of urban transportation modes, many urban areas have the problem of unbalanced traffic demand, which makes accurate prediction of traffic demand very important. However, most of the existing studies focus on improving the prediction accuracy of traffic demand on the single spatial relationship of a single traffic mode, ignoring the diversity of spatial relationships and the heterogeneity of transportation stations in the traffic network. In this paper, we propose a Co-Modal Graph Attention neTwork(CMGAT) framework to uncover the impact of different spatial relationships and traffic mode interactions on traffic demand. Specifically, we first utilize a feature embedding block to capture the semantic information from several features. Then, a multiple traffic graphs-based spatial attention mechanism and a multiple time periods-based temporal attention mechanism are proposed to capture spatial and temporal dependencies in multi-mode traffic demands. Moreover, an output layer is provided to incorporate the hidden states and raw time sequences to predict future traffic demand. Finally, we conduct experiments on two real-world datasets, NYC Bike and NYC Taxi, and the results not only demonstrate the superiority of our model, but also indicate the necessity of considering multiple spatial relationships and traffic modes.