Travel Chain Research Articles

Electric Vehicle Charging Load Demand Forecasting in Different Functional Areas of Cities with Weighted Measurement Fusion UKF Algorithm

The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting.

Balancing Cost and Economic Efficiency: Consider the multi-purpose optimization of green energy market's function in Green energy interactive infrastructure

The increasing reliance on electric micro-mobility vehicles (EMVs) in the delivery industry necessitates efficient battery-swapping infrastructure. This study presents a framework that predicts battery-swapping needs for EMVs using a simulation model that considers the entire travel chain of activities. We propose a multi-objective optimization model to strategically determine battery-swapping station locations, balancing construction and transit costs. Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), we identified 35 non-dominated solutions within the Pareto front. For Nanjing City, our model indicated that the construction of an optimal network could be achieved with costs ranging from 2.85 to 4.94 million Yuan, corresponding to battery-swapping trip costs between 9700 and 197,000 Yuan. The simulation predicted a daily battery-swapping demand of 675 instances, with peak hours at 11:00 a.m. (301 swaps) and 5:00 p.m. (198 swaps). Sensitivity analysis showed that reducing the charging period from 3 h to 1 h could decrease construction costs by 1.55 million Yuan on average, while maintaining a consistent battery-swapping travel cost of around 23,000 Yuan. Additionally, a 5 % increase in battery-swapping penetration rate led to an average increase of 480,000 Yuan in construction costs and 847 Yuan in travel costs. This study integrates demand forecasting and infrastructure optimization, providing actionable insights for planning and managing battery-swapping stations.

Intercity Traffic Travel Mode Identification Method Based on Mobile Signalling Data

With the popularity of mobile devices, the signalling data generated by them provides significant opportunities for studying intercity travel behaviour in terms of data scale and information continuity. However, due to the low quality of the data in spatial accuracy, temporal frequency, and traffic semantics, the accuracy of identifying individual travel modes is low and it is difficult to extend to complex traffic scenarios. In this paper, we propose a new framework for identifying individual intercity travel modes based on mobile signalling data. The framework includes components for data pre-processing, geo-information mapping, feature and attribute extraction, and travel mode recognition. We utilize a comprehensive detection model to identify users’ multimodal intercity transport behaviour. Using two modules, Random Forest Embedding (RFE) and Bidirectional Long Short-Term Memory (Bi-LSTM), the model can capture the spatiotemporal characteristics and complex multi-stage associations in intercity travel chains. A large-scale mobile phone dataset from Jiangsu Province, China, was used for verification. The results showed that, on average, the method was able to detect travel mode with 92% accuracy. This study provides valuable support for further research on individual travel behaviour and the enhancement of transportation planning.

Spatial-temporal Distribution Prediction of Electric Vehicle Charging Load Considering Vehicle-road-station-network Integration

Aiming at the problem of inaccurate prediction of spatial-temporal distribution of electric vehicle charging load due to insufficient consideration of vehicle-road-station-network interaction, a prediction model of spatial-temporal distribution of electric vehicle charging load based on gravitation model was proposed. Firstly, considering road network traffic flow and ambient temperature, the relationship between external environment and energy consumption of electric vehicles is analyzed. Secondly, considering the influence of external environmental factors such as temperature on user travel, a travel chain model based on travel intention modification is established. Finally, the selection model of EV charging station based on gravitation model is established. The simulation results show that the proposed model can take into account the interaction of EV, road network, charging station and power grid, and accurately calculate the spatio-temporal distribution of EV charging load, and analyze the characteristics of EV charging demand load in multi-regions.

Creating architectural advantage in the airline industry: Strategic options for airlines to migrate value

Despite their central role in the industry, airlines have been consistently unable to earn their capital costs. The academic literature has noted the airlines’ limited success in capturing value, offered plausible explanations for it, but has been remarkably silent regarding strategic options for airlines to change their situation. The present paper addresses this gap by analysing the vertical embeddedness of airlines into the industry structure and focusing on the dynamics of the interfaces with adjacent industry segments. Using secondary data from industry reports and primary data from semi-structured interviews with industry experts, our analysis aims at identifying the main drivers and strategic options for value migration. The interfaces between airlines and distribution partners as well as airports show the highest potential for value migration, primarily for well-positioned low-cost carriers or larger multi-airline groups. To obtain that advantage, however, airlines need to extend their offering beyond the flight segment and reduce their replaceability by leveraging customer data along the entire travel chain, e.g., by sharing customer insights between segments and, thus, accessing additional revenue potential. For smaller, independent airlines, leveraging their -if present- dedicated niche strategy, will be the only lever to survive in the long-run. With its systemic perspective, this paper complements the currently fragmented literature on airline vertical strategies and suggests four generic strategic options for value migration to the airline segment.

Exploring hierarchy shift of travelers’ public transport dependence using an improved Apriori algorithm

ABSTRACT Special external environments will lead to significant changes in the use behavior and dependence degree of different PT travellers, but it is difficult to analyze the mechanism of the hierarchy shift of travelers’ public transportation (PT) dependence. Exploring travelers’ dependence on PT is conducive to understanding individuals’ travel choice behavior and optimizing PT operation organizations. Developing methods for analyzing the internal causal relationship between travelers’ dependence on PT and the key influencing factors under the special condition is an issue. Therefore, the individual travel chains are constructed by associating and matching the multisource PT big data and travel survey data. Thereafter, the K-means algorithm and an improved Apriori algorithm are developed to mine the frequent association rules of groups, and a framework of cross-hierarchy policy implications is derived based on the differences in association rules. Finally, the stated preference survey method is used to measure the effectiveness of the policies.

Triggers for feelings of insecurity and perceptions of safety in relation to public transport; the experiences of young and active travellers

ABSTRACT A key aspect of an attractive and accessible public transport system is that passengers feel safe and secure, not least from a travel chain perspective. The aim of this paper is to provide an increased understanding of triggers for feelings of insecurity and perceptions of safety in relation to public transport, with a special focus on larger public transport nodes in the city of Malmö, Sweden. The study draws on a map-based online survey (public participatory GIS) which was designed to allow people to contribute their experiences and reflections. An online survey was distributed through social media, and the collected data is of both a quantitative and a qualitative nature. The sample consists of 290 students and workers between the ages of 21–35 who frequently use public transport. The findings show that one in ten respondents have often to very often avoided public transport because of feelings of insecurity. Thematic analysis revealed a variety of triggers, covering aspects from the physical dimension to the social dimension. Some examples include the design of bus stops, the surrounding traffic environment, and other people. The presence of guards and the police force seems to increase the feelings of insecurity for some, while the opposite was true for other respondents. The findings emphasize the importance of further research to illuminate different parts of a journey at a micro level, i.e. from the person-environment relationship, as well as to further highlight the issue of who transit environments are planned and designed for.

Оптимізація транспортно-логістичної системи задля спрощення переміщення населення сільської місцевості

The purpose of the article is to examine existing data for the selection of survey areas, data collection for the mobile network at the level of travel chains, and analysis of travel patterns. Travel demand in three rural areas with different population densities is considered, and requirements for new mobility solutions based on demand and people's travel habits are established.
 Public transport plays a decisive role in the logistics of territorial communities of Ukraine. However, inadequate provision and implementation of technological advances have left rural areas behind and heavily dependent on private cars. This article describes how to improve the availability of transport services in rural areas, where the demand for travel is often underestimated. The demand for travel in three rural areas with different population densities was analyzed, and the requirements for new mobility solutions based on demand and people's travel habits were determined.
 There is a need to access a higher level of subsidies if on-demand mobility services cannot attract an adequate level of demand so that rural and semi-rural communities have access to the same level of services and opportunities available to urban areas. Most of these services are deployed in urban settings with high travel demand and population density. Research on regions with low demand is rarely considered.
 Many on-demand services are community-based, where demand is determined based on local knowledge and specific needs. Those mobility services that are more commercially oriented use qualitative market research to determine demand for specific population segments and specific locations.
 Social isolation is exacerbated by lack of accessibility, roads and public transport, but is also linked to socio-demographic factors.
 It is suggested that consideration be given to removing barriers to commercially viable new mobility services in rural areas by: using data-driven approaches to understand typical daily travel patterns; identifying new mobility services that can be deployed for commercial purposes and those that will require ongoing financial support to provide socially necessary services.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

Barriers and facilitators of public transport use among people with disabilities: a scoping review.

Barriers to public transport use may be experienced differently by people with various types of disabilities (e.g., physical, intellectual, cognitive, sensory). Thus, it is important to identify the variable needs within each element of the travel chain. For example, the unavailability or low volume of auditory announcements in a stop or station or on the public transport vehicle may be a barrier to people with visual disability who rely on hearing the information. Consequently, this could provoke negative emotions and unpleasant experiences, which may not be the case for people with physical disabilities. The primary objective was to describe the barriers and facilitators to using public transport experienced by people with disabilities (PWD). The secondary aim was to explore experiences in terms of self-efficacy and satisfaction, when using public transport among people with disabilities. A scoping review was conducted. The search was performed in MEDLINE, TRANSPORT DATABASE, PsycINFO, EMBASE, and WEB OF SCIENCE from 1995 to 2023. Of 6,820 citations identified, 34 articles were included in the review for extraction. The main physical and social barriers included lack of ramp, long walking distance, long waiting time, unavailability of information at bus stop or station, and drivers' negative attitudes towards PWD. Personal factors that prevented the use of public transport included lack of confidence, and decreased satisfaction with public transport use. Strategies such as providing ramps on public transport vehicles, availability of kneeling buses and courtesy of bus drivers, and travel training were considered as enablers to the use of public transport that can lead the improved self-efficacy and satisfaction. In conclusion, this review identified the physical and social barriers and facilitators in travel chain, and highlighted issues related to lack of confidence or self-efficacy and decreased satisfaction when PWD and older adults are using public transport. Identifying and understanding the barriers and facilitators to the use of public transport by PWD is a milestone that may help policy makers and transport operators around the world to develop and implement interventions enabling access, use and inclusion of this mode of transport, as the experiences of PWD when using this mode of transport have an impact on their well-being.

Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles

As the delivery sector increasingly relies on electric micromobility vehicles (EMVs), the urgency for efficient battery-swapping infrastructure becomes critical. This study develops a comprehensive framework for predicting battery-swapping demand for delivery EMVs (DEMVs) based on an activity-based travel chain simulation model and devises a multi-objective optimization model for the strategic placement of battery-swapping stations. The simulation model integrates submodules such as the EMV generation and attraction model, Agent-based EMV travel chain, and EMV and battery-swapping behavior model to capture the nuanced travel patterns and battery-swapping demand. Leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the study optimizes the network design of battery-swapping stations considering both construction and travel costs. A case study in Nanjing City, representative of the diverse delivery sector's operations, substantiates the simulation's accuracy, maps out the spatiotemporal distribution of swapping demand, and analyzes the Pareto optimal set derived from the optimization model. Sensitivity analysis focuses on the facility planning model, assessing how uncertainties in swapping demand and battery charging rates within stations impact operational efficacy. This research melds demand forecasting with infrastructure optimization, providing actionable insights for the planning and management of battery-swapping stations.

Hacia un Turismo para la diversidad

This article aims to synthesise a larger ethnographic work on the circumstances surrounding the experiences of people with specific accessibility requirements when they indulge in tourism, carried out during 2015 and 2016 on the Alicante coast – Santa Pola, Elche, Benidorm – and the south of Tenerife – Los Cristianos, Arona –. The effects produced by the physical and attitudinal barriers that they have to face are analysed. The objectives outlined in this paper are: to know the factors that act as invisible barriers preventing people with special needs from fully enjoying leisure and tourism; to know why businessmen in the sector show such little interest in attracting clients with special needs; explore whether the image and symbology of special needs has an impact on the tourist experience of people in this category and to clarify whether accessible tourism constitutes a niche or a new market segment. In this regard, we provide passages from the ethnography on which the study is based, through in‑depth interviews and participant observation. Likewise, the progressive application of new technologies to the travel chain accessibility is helping to set up, with greater security and dynamism, a tourism that has set a course towards diversity.

Activity-Based travel chain simulation for Battery-Swapping demand of electric micromobility vehicles

Intelligent battery-swapping services for electric micromobility vehicles (EMVs) are expanding from To-business to To-customer sides, offering consumers greater convenience while addressing charging challenges. This study proposes an activity-based travel chain simulation framework to predict battery-swapping demand for ordinary and delivery EMVs. A case study in Nanjing City shows that temporal distribution of EMV battery-swapping demand is related to travel patterns, while spatial distribution is correlated with EMV generation and traffic zones. Sensitivity analysis examines the impact of swapping penetration, initial power, and swapping threshold on the swapping demand. Findings suggest that increasing swapping penetration by 1% leads to a rise of 692 and 139 in one-day swapping demand for ordinary and delivery EMVs, respectively. Furthermore, higher initial power leads to lower one-day swapping demand, while increasing the swapping threshold leads to higher swapping demand. These findings offer important insights for battery-swapping station planning and operation management.

Research on the construction of passenger travel chain based on utility

Large-scale integrated passenger transportation hubs are places where multiple modes of transportation are used to switch within the city, and they are also the convergence points of traffic inside and outside the city, and it is important to ensure their smooth under different traffic conditions. Firstly, the passenger travel utility in hubs model and transportation system utility model are proposed; then the travel demands of various types of passengers are accurately identified based on data mining and user portrait technology when the passenger volume is low, and the typical user priority filtering recommendation algorithm and information dynamic update strategy are proposed with the goal of optimal passenger travel utility; then for the large passenger volume scenario, the optimal transportation system operation utility is the goal for us, as the passenger volume increases continuously, the transportation system service is close to the critical state, a bi-level optimization model is established to avoid the resonance of the transportation system and guarantee the balance of optimal utility between the passenger side and the transportation system side. After the internal test of ‘Smooth Hongqiao’ APP, the travel time of passengers in the hub reduced by 5.6%, the utility value of the travel chain increased by 16%, and the saturation of the surrounding road network reduced by 5.7% after the above methods are flexibly applied to build the passenger travel chain according to different traffic scenarios.

Study on optimal configuration of EV charging stations based on second-order cone

The Electric Vehicle(EV) charging station frequently participates in the daily regulation and operation of the power distribution system, whether the installation position is reasonable and the configuration capacity is appropriate, it will directly affect the operation strategy and operating conditions of the distribution system, and then affect the economic and environmental benefits of the distributed power supply and EV load connected to the power grid, based on the analysis of fuel vehicles, EV travel characteristics and electrical geography coupled state, by fitting the charging data of EV with travel chain, more accurate EV charging load can be obtained. Secondly, the optimal allocation model of EV charging stations is established to optimize the grid loss, and the nonconvex problem is solved by combining the second-order cone relaxation technique with the distflow power flow model, the configuration scheme of EV charging station is obtained. Finally, taking an EV charging scene in a certain area as an example, the optimal configuration of the charging station is simulated by setting up two types of charging station: conventional charging station and conventional charging station and fast charging station, the effectiveness of the proposed configuration scheme is verified. The optimal configuration scheme of EV charging station described in this paper provides a theoretical basis for charging station planning and capacity expansion.

Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

AbstractIn order to solve the problem that the current charging load forecasting accuracy is not high, it is difficult to simulate the actual charging load distribution of Electric Vehicles (EVs), and it is impossible to reasonably predict the future load, a charging load forecasting model based on Sparrow Search Algorithm (SSA) improved Random Forest Regression (RFR) is proposed. The SSA is used to enhance the ability of global optimization and local exploration. Combined with the advantages of the RFR model, such as low generalization error, fast convergence speed, and few adjustment parameters, the SSA was used to optimize the parameters of the decision tree number and the number of split nodes in the RFR, and the optimal value of the parameters is obtained, so as to obtain the optimal performance of the RFR. Firstly, based on the concept of travel chain and conditional probability distribution, the user's travel habits are described. Monte Carlo simulation method was used to simulate the driving, parking, and charging behaviours of a large number of EVs in different regions, so as to obtain the charging load of EVs in different regions. Then, a charging load forecasting model based on SSA improved RFR is established. Monte Carlo simulation results are used as sample data to predict the charging load of EVs in different regions. Finally, taking a certain area as an example, the experimental results show that the charging load prediction model based on Sparrow Search Algorithm improved Random Forest Regression (SSA‐RFR) can accurately predict the charging load of EVs in different regions, and the charging load of different regional types is obviously different. Compared with the RFR model and other literature models, the SSA‐RFR model has better prediction accuracy, which verifies the feasibility and superiority of SSA‐RFR model in EVs charging load prediction.

Battery swapping demand simulation for electric micromobility vehicles considering multi-source information interaction and behavior decision

The battery swapping mode has the advantages of convenience and battery controllability, which can alleviate charging problems with electric micromobility vehicles (EMVs). The layout of battery swapping facilities and scheduling management can be carried out by accurately analyzing the high-resolution spatial-temporal distribution of battery swapping demand for EMV. This study establishes a prediction model framework for battery swapping demand of EMV using Monte Carlo simulation based on travel chains considering multi-source information interaction and behavior decision. Using real residential travel survey data of Nanning City, China and an empirical analysis with the city as a case study, the results show that the prediction framework proposed in this study is reliable. The temporal distribution of EMV battery swapping demand is closely related to the spatial distribution of travel. In addition, the demand characteristics of both centralized and decentralized battery swapping stations are evaluated separately. The peak intensity of the centralized mode is 18% greater than that of the decentralized mode when the battery swapping penetration is 35%. When the battery swapping penetration rate is low, decentralized mode can meet the peak swapping demand, and as the penetration rate increases, the effect of centralized mode is reflected. Finally, the total swapping path time considering multi-source information interaction and behavior decision is reduced by 7.8%. These findings allow for the study of battery swapping station planning and transportation planning.

Travel Mode Choice Behavior for High-Speed Railway Stations Based on Multi-Source Data

The service quality of connecting travel mode at high-speed railway (HSR) stations plays an essential role in the travel chain. Most travel choice behavior studies are based on survey data and focus on special scenarios, such as going to work or school. Few studies have addressed travel mode choices for connecting to HSR stations. Based on multi-source data, this study aims to investigate the influences of travel time, cost, reliability, detour, and land use on connecting mode choice behaviors at HSR stations. Taking the Xi’anbei railway station as an example, this study analyzes the behavior characteristics of connecting modes including conventional buses, subway transit, and taxis, and builds a connecting travel mode choice behavior model for arrival/departure at HSR stations based on a generalized additive model (GAM). The results show that connecting travelers at HSR stations prefer to choose subways and taxis during the morning and evening peak hours. The perceived costs for arriving travelers are less sensitive than those for departing travelers. Arriving travelers are significantly more sensitive to reliability than departing travelers. Connecting travelers are less sensitive to cost and more sensitive to reliability than commuting travelers. The research results can provide suggestions and guidelines for connecting facility planning in HSR stations.

Public Transport Accessibility for People With Disabilities: Protocol for a Scoping Review.

Transportation is essential for people of all ages and backgrounds to live a fulfilling and satisfying life. Public transport (PT) can facilitate access to the community and improve social participation. However, people with disabilities may encounter barriers or facilitators in the whole travel chain that can lead to negative or positive perceptions in terms of self-efficacy or satisfaction. These barriers may be perceived depending on the nature of the disability. Few studies have identified PT barriers and facilitators experienced by people with disabilities. However, findings were focused mainly on specific disabilities. Access requires broader considerations of barriers and facilitators for various types of disabilities. This scoping review aims to describe the barriers and facilitators to the use of PT experienced by people with various disabilities in the whole travel chain and to explore perceived experiences, self-efficacy, and satisfaction when using PT. A scoping review will be conducted using Arksey and O'Malley's framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. The literature search will be conducted using the electronic databases MEDLINE, Transport Database, and PsycINFO via Ovid platform, Embase, and Web of Science from 1995 to 2022. Two reviewers will independently identify studies based on inclusion (published in English or French, outcomes on PT accessibility for people with disabilities, peer-reviewed or guideline reports or editorials) and exclusion (no full text, focused on a technology system, outcome validation study, study on no-fixed route PT accessibility, etc) criteria and extract the data. When a study has addressed the accessibility of multiple modes of PT, including fixed-route PT, it will be retained. However, only data on fixed-route PT will be extracted. Any related systematic reviews identified through the search will be retained, and the reference lists will be hand-searched and screened for inclusion criteria. The search we performed on July 21, 2022, in the databases mentioned above allowed us to retrieve 6399 citations. Of these citations, 31 articles were identified, and data extraction was performed. As of March 11, 2023, we have started data analysis. The findings will be synthesized narratively to summarize the barriers and facilitators to PT, perceived experiences with PT, self-efficacy for using PT, and satisfaction with PT according to the Human Development Model-Disability Creation Process conceptual framework. The results of this scoping review could lead to a better understanding of the potential barriers and facilitators to the use of PT by people with various types of disabilities and how negative or positive experiences throughout the travel may influence their self-efficacy and satisfaction. The results may be used to provide recommendations to PT providers and policy makers to work together to make PT accessible, usable, and inclusive for all people with disabilities. Open Science Framework OSF.IO/2JDQS; https://osf.io/2jdqs. DERR1-10.2196/43188.