Comparative Analysis of App-Based Travel Diary and Self-Reported Behaviour: A Case Study from Glasgow, UK

As the complexity of travel surveys increases, ensuring participant engagement is key to maintaining data reliability. Traditional methods bear certain limitations, and automated, app-based data collection emerges as a promising alternative, capable of gathering more detailed and precise travel data over extended periods. Our study investigates the potential and challenges of app-based data collection methods in contrast to traditional surveys for transportation policy-making. Data from 383 individuals using the TravelAI app for over a week was analysed, with 294 of these individuals also completing a one-day travel diary within the same TravelAI app. Initial evaluation exposed some inconsistencies in the self-reported data. Stated trip times were inaccurate due to confusion with 24-hour time format and several trips had inadequate details regarding trips postcode locations. For comparative analysis, two methods were employed to match trips across both datasets. First, by setting different time thresholds for the discrepancy between stated and detected trip times. A two-hour threshold yielded a match for 969 out of 1662 trips (72% mode detection accuracy). The second method incorporated a spatial match in addition to time thresholds. If the detected location was within the user-provided postcode boundaries, the trip was considered matched. This method yielded a lower count of matched trips (368); however, mode detection accuracy rose to 83%, suggesting that precise trip matching yields reliable results. Notably, there was a higher match rate among younger participants (16-45), implying potential unreliability in older groups' self-reported data. The low matching of the trips also resulted from users giving a wrong date for their travel diary as for some users the trips matched better with app-data of another date rather than the stated date, indicating further unreliability of the traditional survey diary data. When comparing both datasets, differences were more pronounced among iOS users compared to Android users, attributable to iOS's stricter privacy controls for apps. For iOS users to grant full access to their location data, additional steps in the settings are required. This discrepancy was even more evident among older age groups, compared to younger ones. This highlights a potential limitation of app-based methods, emphasizing the importance of careful installation with all necessary permissions granted by the users. The app-based data provided more detailed insights, recording separate legs of each trip, unlike the traditional self-reported data. These findings highlight the complexities in data collection choices, illuminating the promise and limitations of app-based methods in capturing accurate and comprehensive travel data

Did safe cycling infrastructure still matter during a COVID-19 lockdown?

The UK government introduced strict measures (including asking people to work from home and a lockdown) to slow the spread of COVID-19 by limiting people’s movement. This led to substantial reductions in traffic, making roads much safer for cyclists. This provides a unique opportunity to study the role played by safe cycling infrastructure. Many UK cities have provided cycling infrastructure to improve safety and encourage cycling. However, access to safe cycling infrastructure varies across neighbourhoods, potentially contributing to inequality. Since roads became safer due to the unprecedented reduction in traffic during the lockdown, safe cycling infrastructure may not play a significant role during this period. On the other hand, safe cycling lanes are often connected to amenities, potentially attracting cyclists even if they confer no additional safety benefit. That is, connectivity might matter more than safety. In this study, we utilised crowdsourced cycling data and regression models to examine the extent to which cycling intensity for non-commuting purposes changes with different types of cycling infrastructure in the city of Glasgow, Scotland, UK. In addition, we selected some areas with large increases in cycling intensity and examined the surrounding environments using Google Street View. Our results showed that non-commuting cycling activities increased significantly after the government interventions on both typical roads and safe cycling lanes while much higher increases were observed on safe cycling lanes than on other roads. A further analysis showed that there were large increases in cycling volumes on both typical roads and safe cycling lanes with good amenities and connectivity, highlighting the importance of these factors when building new cycling infrastructure. Since safe cycling lanes are not equally accessible to people, providing temporary cycling lanes during the pandemic considering these conditions could encourage people to cycle more, and thereby improve their health

Analyzing Inter-modal Competition between High Speed Rail and Conventional Transport Systems: A Game Theoretic Approach

AbstractA methodology is developed in order to assess the viability of transport infrastructure investment in the form of High Speed Rail (HSR). Public transportation mode operators such as HSR, conventional trains and buses, maximize their profits by varying prices and frequency for a given demand and infrastructure cost. In this study, the price competition between different operators is taken into consideration and the change in the existing market equilibrium due to the entry of the new mode is studied using the game theoretic approach. Hypothetical data for a particular route is used for game-based analysis. In this multiplayer game, the effect of introducing the new mode of transport on the Nash equilibrium is studied taking into account the competition between the other modes of transportation. The analysis of market share for the modes has been carried out using heterogeneity of the passengers based on the concept of Value-of-Time (VOT). The passengers are assumed to be intelligent and rational in choosing the mode that minimizes their generalized travel cost, which is a function of travel time weighted by the individual VOT and the monetary cost associated with the mode of travel. Thus, different combinations of entry and response strategies are studied for HSR and existing modes, and the impact of introduction of HSR is assessed in terms of profit, thus, reflecting on the sustainability and financial viability of the transport infrastructure investment

Can Smartphone Location Data at the Point Level be Used to Estimate Traffic Volumes?: A Methodological Evaluation

The effective development of transport planning strategies relies on timely and comprehensive urban travel information. Manual or automatic traffic counts form a key input into many models but are available only for a limited set of locations and at a low frequency. The emergence of digital footprint data, particularly location data at the point level obtained from smartphones, has raised the possibility of replacing or supplementing traditional analytical methods and sources. However, the suitability of the data for these purposes is currently poorly understood. This study aims to assess the accuracy of estimating traffic counts and travel direction volumes by utilizing a sample of high-resolution smartphone location data. Counts will be derived and compared to 44 manual count points published by the Department of Transport in 2019 at various locations in and around Glasgow. The location data is provided by a private organization, Huq, which comprises 22 million records from 19,000 unique users for the same year in the study area. Two main challenges are addressed in the paper. The first is effectively extracting travel activity from the location data while mitigating the noise stemming from non-travel activities and imprecise or sparse location estimates. To address this, we construct and test a series of systematic buffers around traffic count points based on the characteristics of the road network and the immediate built environment. The second challenge concerns the heterogeneity in the quality and volume of the data produced by app users. We further examine whether it is possible to refine the estimates by inferring individual trips and the direction of travel based on the patterns of the location data. Preliminary results indicate that the proposed methodology explains between 36% and 70% of the variability of the manual traffic counts in a regression framework. This study establishes a methodological precedent and suggests directions for future research. Potential avenues include investigating additional years or cities, incorporating more temporally detailed traffic count data, integrating CCTV-based counts, evaluating alternative location data sources, and focusing on active travel modes

Analyzing competition between High Speed Rail and Bus mode using market entry game analysis

The introduction of High Speed Rail (HSR) changes the dynamics of the passenger transport therefore understanding the market scenario after the entry of the HSR is of utmost importance. In this study, a market entry game is analysed in the context of High Speed Rail competing with the bus mode on the Bangalore-Mysore corridor. The game is modelled as an extensive form game whose outcome will determine the strategies of the players in the competitive scenario. A discrete choice model is constructed using revealed and stated preference data to compute the modal share in the hypothetical scenario. Utilities in terms of profit for each mode is calculated using assumed cost functions for different strategies of the mode operators. These utilities are then used to form the basis for the extensive form game for the market entry. The extensive form game (with and without perfect information) is analyzed by computing the sub game perfect Nash equilibrium of the game which determines best strategies for each player under different demand scenarios. This study therefore provides a scientific tool for policy makers to analyze best strategies for players under different demand scenarios thereby aiding in decision making. (C) 2017 The Authors. Published by Elsevier B.V

Competition between High Speed Rail and conventional transport modes: market entry game analysis on Indian corridors

This paper investigates the competition between HSR and the incumbent with vertical service differentiation for Indian corridors. As Indian government plans to invest in this new technology, strategic decisions pertaining to type of corridor, speed of HSR, HSR technology, given the competition scenario on that corridor becomes vital. The strategic interactions between the operators are modelled as a three staged game between the entrant and the incumbent considering the competition over fare and frequency to maximize different objective functions i.e. Profit and Social Welfare. Speed of HSR is taken as a strategic variable in the game with two levels of high speed, {Low-H, High-H}. This model is applied on two corridors of India of different length i.e. Bangalore-Delhi (competition with airlines with length of corridor being 2400 km) and Bangalore-Mysore (competition with bus with length of corridor being 150 km). Revealed and stated preference surveys are conducted for the passengers traveling on these corridors and a discrete choice model was estimated for both the corridors. These models were used to determine the modal share in the new hypothetical scenario which were in turn used in defining objective functions such as profits and social welfare. Various game scenarios characterized by sunk and variable cost of the modes are formulated and equilibrium for all demand levels is computed for both the corridors for these different objective functions. Results demonstrates variation in Nash equilibrium for different game scenarios and hence indicates the importance of incorporating speed as a strategic variable. Changing the objective function to social welfare maximization results in different equilibrium solution for Bangalore-Delhi corridor. Thus, impact of different combinations of demand, cost structures and objective functions are explored on the market equilibrium thereby providing interesting insights in this area

A game-theoretic approach to analyse inter-modal competition between high-speed rail and airlines in the Indian context

In this paper, a game-theoretic framework is applied in order to model the competition scenario between high-speed rail (HSR) and airlines in the Indian context and assess the impact of speed and passengers' characteristics on the equilibrium of the game. The competition is modelled in terms of the fare and frequency offered by the operators to maximize their profits. The speed of HSR is taken as an additional strategic variable in the game with three levels of high speed: low, medium, high. A three-stage game is formulated with the entrant playing its speed strategy in the first stage followed by optimal fare and frequency selection by both the modes. Passengers are considered to be heterogeneous in nature by assuming a continuous distribution of the value of time. Numerical simulations indicate that the dominant strategy for airlines and HSR is based on accommodation and medium speed, respectively

Estimating OD matrices using mobile app data: a novel approach to address longitudinal mobile data issues

Motivation and objective: Mobile phone apps (MPA) are crucial for urban mobility research due to their detailed spatio-temporal data. However, the volume and quality of GPS data harvested from smartphone apps is affected by changes in data privacy protection policies, alterations in app data collection methods by the data provider, and yearly variations in user engagement and app usage. This inherent variability can skew research outcomes significantly. We highlight these pronounced discrepancies encountered while constructing origin-destination (OD) matrices using mobile data sourced from Huq.io for Glasgow city from 2019 to 2022. Such discrepancies underscore the challenges in achieving longitudinal consistency in urban mobility research. To address these challenges, we propose a novel methodology that incorporates individual activity levels from the datasets to weight the trips annually enabling longitudinal comparisons. Study design/data and methods/approach: Using Python's scikit mobility package on the MPA dataset, we identified stay locations at 200m and 500m thresholds, defining trips as movements between these locations. The dataset grew from 10,626 users and 22.7 million observations in 2019 to 30,436 users with 169.5 million observations in 2020, 30,268 users with 356.3 million observations in 2021, and 8,814 users with 105.9 million observations in 2022. These trips were weighted by each individual's Scottish Index of Multiple Deprivation (SIMD) and geographical zone, using users’ home locations to enhance the O-D matrix's representation of population mobility patterns. Home locations were estimated via a method detailed in Sinclair et al. (2022). When a user's home location was indeterminable, their trips remained unweighted in the dataset. The dataset shows variance in user numbers and data over four years due to activity level differences. Huq favoured users and apps with higher data output. To address this, we introduced a novel method, scaling trip data by user activity frequency. For example, a user active for 183 days with 500 trips is scaled to 1000 trips for a year, enabling consistent longitudinal data comparisons. Users active for less than a week were excluded to ensure reliable travel behaviour extrapolation. Individual weights were calculated annually and quarterly to refine scaling and reduce extrapolation effects. After applying these adjustments, trips were aggregated by user and for each O-D pair to form a preliminary OD matrix. A global scaling factor was then applied, adjusting trips to represent Glasgow City Region’s (GCR) entire population, considering Huq's user base. Findings/results: We analysed OD trips at three levels: 1) Detected trips from MPA data; 2) SIMD weighted trips adjusted for SIMD, council weights, and GCR population; and 3) Activity and SIMD weighted trips incorporating all adjustments. Figure 1 plots these trips quarterly from 2019 Q3 to 2022 Q4. Detected trips show an increase from 2019 to 2021, with a decrease in 2022, contradicting the expected pandemic mobility reduction. SIMD and global weighting alone did not correct this trend. However, incorporating activity weights aligned trip trends with expected patterns, notably reflecting the reduction in 2020 Q2 during the UK lockdown. This underscores the necessity of accounting for individual activity variations in longitudinal analyses or when seeking absolute numbers. Validating the OD matrix against Scottish household survey data further affirms our methodology's accuracy. Originality/value: Our methodology introduces a novel approach by integrating individual activity levels and SIMD weights, offering a more accurate representation of urban mobility patterns, setting a new standard for longitudinal urban mobility analysis using MPA datasets

Analyzing intercity modal choice and competition between High Speed Rail (HSR) and other transport modes in Indian context

In this study, a theoretical framework is developed in order to assess the viability of transport infrastructure investment in the form of High Speed Rail (HSR) by assessing, the mode choice behaviour of the passengers and the strategies of the operators, in the hypothetical scenario. Discrete choice modelling (DCM) integrated with a game theoretic approach is used to model this dynamic market scenario. DCM is incorporated to predict the mode choice behaviour of the passengers in the new scenario and the change in the existing market equilibrium and strategies of the operators due to the entry of the new mode is analysed using the game theoretic approach. The outcome of this market game will describe the strategies for operators corresponding to Nash equilibrium. In conclusion, the impact of introduction of HSR is assessed in terms of social welfare by analysing the mode choice behaviour and strategic decision making of the operators, thus reflecting on the economic viability of the transport infrastructure investment

Deriving Mobility Insights from Mobile App Data: A Comparative Study on Distance Thresholds for Stay Detection

Urban mobility patterns are crucial for understanding city dynamics, impacting areas such as transportation planning, urban development, and public policy. Traditional data collection techniques, like household surveys, often miss the subtle details of daily travel patterns, especially short, incidental trips, and face challenges in gathering data over extended spatiotemporal scales. In contrast, passively collected data, including GPS data from mobile apps and cellular data, open new avenues for analyzing human mobility and enhancing transportation systems. These datasets, primarily collected for purposes unrelated to transportation, require processing to extract valuable mobility insights. Numerous studies have utilized big data approaches, including the use of cellular data records (CDR), to investigate urban mobility patterns. However, it falls short in spatial precision when compared to GPS data. Consequently, GPS data derived from mobile apps emerges as a promising alternative to CDR data, offering enhanced spatial resolution for more accurate mobility analyses. Though, there is a notable gap in research utilizing GPS data from mobile apps for such studies, or existing research is often limited by a short time frame, undermining the potential benefits of these datasets. To extract mobility patterns from such passive data sources, the first step involves identifying "stays"—periods when the device remains stationary. A trip is then defined as the movement between two consecutive stays, essentially marking the trip's origin and destination. A stay is identified based on the device's lack of movement beyond a predefined distance (for example, more than 200 meters for GPS data from mobile apps or 1000 meters for cellular data) over a specified duration (such as 5 minutes). This approach, known as Trace-segmentation, is supported by various open-source tools, including the Python scikit-mobility package, which facilitates the extraction and analysis of mobility data from passive data sources. The process of identifying mobility patterns heavily relies on the selection of a predefined distance threshold to determine stays, yet there is limited exploration in the literature regarding the optimal distance threshold due to the novelty of these data sources. While some researchers have adopted a 200m threshold, others have opted for a 500m threshold, leading to the question: which threshold more accurately captures mobility patterns? To address this uncertainty, our study aims to analyze stays using both 200m and 500m thresholds and then compare the findings with data from a comprehensive household survey, specifically the Scottish Household Survey, to ascertain the most effective distance threshold for mobility analysis. Additionally, we extracted individual GPS points within these identified stay locations, applying both distance thresholds, to examine spatial behaviours in relation to various types of spatial areas, such as retail, residential, and others. This in-depth analysis is designed to explore how key mobility attributes, like the average duration of stays in various spatial zones or the distribution of travel lengths, are influenced by the choice of distance thresholds. For this study, we utilized data sourced from Huq.io, covering a four-year period from 2019 to 2022. This dataset includes records from 10,626 users totalling 22,716,662 observations in 2019, expanded to 30,436 users with 169,529,841 observations in 2020, 30,268 users contributing 356,306,418 observations in 2021, and 8,814 users generating 105,852,747 observations in 2022. The substantial volume and four-year span of the data make it an invaluable resource for conducting longitudinal comparisons and understanding changes in mobility patterns over time. We used python’s sci-kit mobility package to extract the stay locations from the data. Initial results indicate that the 200m threshold identifies a greater number of trips compared to the 500m threshold across all four years, a trend attributed to the 200m threshold's ability to capture shorter trips, thereby increasing the total trip count. Conversely, the 500m threshold is more adept at recognizing major daily trips of individuals (e.g. home to work trip). The analysis of trip length distributions reveals a notable difference between the two thresholds, with a peak under 750m for the 200m threshold and around 1km for the 500m threshold. Interestingly, the distributions of trip times between the two thresholds show minimal variance. When compared with data from the Scottish Household Survey, the 500m threshold aligns more closely with the survey's trip length figures than the 200m threshold. Nonetheless, both thresholds accurately reflect medium-length trips ranging from 2-3km and 3-5km. A marked difference was observed in trips under 1km when compared to the survey data, potentially underscoring the survey's limitations in capturing shorter, less significant trips. For stay durations, the analysis yielded plausible results, with average durations around 8-9 hours for residential areas and approximately 1 hour for retail spaces, further affirming the reliability of these data sources for mobility research. The comparative analysis underscores that the 500m threshold aligns more closely with survey data, suggesting it may be more suitable for studies focused on significant trips or a city level analysis. However, the 200m threshold can prove effective in capturing shorter trips that are frequently omitted in surveys and not even captured with 500m threshold. Thus, if the research goal is to specifically investigate smaller trip behaviours, the 200m threshold emerges as a viable alternative, offering a nuanced view of mobility patterns not typically reported in traditional surveys. In conclusion, our study underscores the potential of using mobile app-based GPS data to enhance urban mobility analysis. By employing both 200m and 500m thresholds for identifying stays and comparing these with comprehensive survey data, we have provided insights into the granularity and diversity of urban travel patterns. This research not only highlights the importance of selecting appropriate distance thresholds for mobility studies but also demonstrates the value of passive data in capturing a broader spectrum of urban movements