Travel Patterns Research Articles

A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY

Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.

Spatio-temporal analysis on online designated driving based on empirical data

In recent years, mobility-on-demand services have experienced rapid growth and become an integral part of the urban transport system. As one of the prevalent mobility-on-demand services, online designated driving allows car-owners booking professional drivers via mobile apps to deliver private cars (frequently with the car-owners inside) to a designated location for a fee. Online designated driving can play a crucial role in promoting road safety by providing a responsible alternative for those who cannot drive themselves due to drinking or fatigue, but there is still a lack of empirical study on its basic travel patterns. Using massive trip records of online designated driving, this study investigates the collective and individual spatio-temporal travel patterns, and reveals the spatio-temporal impact mechanism of various land-use types on online designated driving demand. Take Beijing as a case study, the results show that unlike ride-hailing trips that primarily occur during the daytime, 93.07 % of online designated driving trips occurs between 18:00 and 7:00 the next day, with the longest peak observed on Friday nights. Meanwhile, the average distance of online designated driving trips is approximately twice as long as that of ride-hailing trips (16.58 km vs. 8.48 km). The collective travel patterns of online designated driving exhibit a shrinking trend in the form of “surface-line-point” over time. Regarding individual patterns, we rigorously demonstrate that 81.55 % of high-frequency users exhibit a “fixed-destination” phenomenon, indicating a clear centripetal tendency. Furthermore, online designated driving is primarily associated with limited land-use types, revealing that it may serve specific target audiences, like travelers returning from entertainment and catering activities after consuming alcohol, or those in need of transportation for business and automotive services. These findings can contribute to refining operational practices and enhancing regulatory measures for online designated driving.

Women’s Mobility and Activity Trends in Urban Areas of Ogun State, Nigeria

In the dynamic landscape of modern life, the movement of people and goods fuels economic progress. However, transportation disparities between genders persist globally. Recognizing distinct mobility needs and behaviours between women and men is crucial. This study delves into the travel and activity patterns specifically among women in urban centers of Ogun State, Nigeria. Employing a cross-sectional community survey design, the study sampled women from four cities in Ogun State. A total of 1,597 respondents participated, with analysis focusing on descriptive statistics. Patterns in women's travel, including trip duration, distance, purpose, and mode of transportation, were examined. Results highlighted purpose-driven travel, predominantly for work, household tasks, and religious engagements within town. Motorcycles, tricycles, and walking were common modes of transportation, influenced by factors like trip speed and purpose. Additionally, leisure and visiting friends and relatives constituted frequent out-of-town travel motives. Three distinct daily activity patterns emerged. Addressing challenges like limited access to private vehicles, inadequate infrastructure, and security concerns is vital to enhancing women's travel experiences in the region. Keywords: Women’s mobility, Activity patterns, Urban centres, Ogun State.

Peaked too soon? Analyzing the shifting patterns of PM peak period travel in Southern California

Daily vehicle travel collapsed with the onset of the COVID-19 pandemic in early 2020 but largely bounced back by late 2021. The pandemic caused dramatic changes to working, schooling, shopping, and leisure activities, and to the travel associated with them. Several of these changes have so far proven enduring. So, while overall vehicle travel had largely returned to pre-pandemic levels by late 2021, the underlying drivers of this travel have likely changed.To examine one element of this issue, we analyzed whether patterns of daily trip-making shifted temporally between the fall of 2019 and 2021 in the Greater Los Angeles megaregion. We used location-based service data to examine vehicle trip originations for each hour of the day at the U.S. census block group level in October 2019 and October 2021. We observed notable shifts in the timing of post-pandemic PM peak travel, so we examined changes in the ratio of mid-week trips originating in the early afternoon (12–3:59 PM) and the late afternoon/early evening (4–7:59 PM).We found a clear shift in the temporal distribution of PM trip-making, with relatively more late PM peak period trip-making prior to the pandemic, and more early PM peak trip-making in 2021. The peak afternoon/evening trip-making hour shifted from 5–5:59 PM to 3–3:59 PM. We also found that afternoon/evening trip-making in each year is largely explained by three workplace-area/school-area factors: (1) the number of schoolchildren in a block group (earlier); (2) block groups with large shares of potential remote workers (earlier), and (3) block groups with large shares of low-wage jobs and workers of color (later, except for Black workers in 2021). We found the earlier shift in PM peak travel between pre- and late-pandemic periods to be explained most by (1) higher shares of potential remote workers and (2) higher shares of low-wage jobs and workers of color. These findings suggest that the rise of working from home has likely led to a shift in PM peak travel earlier in the afternoon when school chauffeuring trips are most common. This is especially true for low-income workers and workers of color.

Travel behavior and system dynamics in a simple gamified automated multimodal network

Automated Vehicles (AVs) are poised to disrupt travel patterns and the sustainability of transportation networks. Conventional methods for studying these changes, such as stated preference surveys and agent-based simulations, have limitations. Serious games offer a promising alternative, providing a controlled and engaging environment for investigating travel behavior. In our study, 200 participants, grouped into sessions of 10, engaged in a competitive serious game simulating 50 daily choices of travel mode and departure time across three automated options. Two scenarios were examined: one with recurring congestion and another with nonrecurring congestion. Automated transit had fixed schedules, while private and shared rides could adapt to a congested bottleneck. Results revealed that ridesharing dominated, reaching 60% mode share under recurring congestion, displacing transit, and a comparative equilibrium emerged between shared and private rides. In the nonrecurring congestion scenario, ridesharing dropped to 37%, and a comparable multimodal equilibrium developed. Participants rarely achieved the optimal score, attaining a maximum of 88% of its potential. This study highlights a policy paradox: unregulated AV traffic can reduce transit use, exacerbate recurring congestion, yet necessitate increased transit investment to address nonrecurring congestion, confirming the Downs-Thomson paradox. Creating appealing mass transit alternatives is imperative to ensure efficiency and sustainability in the era of automated mobility.

Characterising travel behaviour patterns of transport hub station area users using mobile phone data

Understanding the travel behaviour of transport hub users is vital for improving transport services. Although previous research has explored passenger behaviour in and around transport hubs, there is a lack of comprehensive studies on travel patterns within hub station areas. To this end, this study harnessed mobile phone data to analyse the travel behaviour within a hub station area, using Beijing South Railway Station as a representative case. A series of recognition criteria was set up based on spatio-temporal information to categorize users within the station area. We categorised station area users into transport hub users (THU) and non-transport-hub users (NTHU) and examined their distinct travel patterns. The results reveal distinct travel patterns for THU and NTHU. NTHU are predominantly concentrated in and around the station area. THU, however, display longer trip distances and more widely distributed endpoints. In terms of travel time distribution, NTHU show evident morning and evening peak phenomena, while the travel time distribution of THU fluctuates throughout the day. The study also employed association rule analysis to illustrate strong connections between the station area, Beijing's core, central areas, and even urban fringe areas. The results reveal that the station area is most closely connected to the core and central areas of Beijing, from where many users are attracted to the station area. In addition, some urban fringe areas also have strong connections with the station area. These findings inform urban transport planning and personalised travel services.

How far are cities from the x-minute city vision? Examining current local travel behavior and land use patterns in Montreal, Canada

In the past three years the 15-minute city planning concept has captured the attention of policy makers and the public worldwide. Some regions have included it in their planning goals and others modified it to 30 min to make it more attainable in their local context. The goal of this research is to measure whether the 15- or 30-minute city goals align with current travel behaviour and land use patterns in the North American context using Montreal, Canada as the case study. In doing so, we look at which destinations are cultivating local travel patterns consistent with the 15- and 30-minute city concepts and identify any unique qualities of these areas using the 2018 Montreal origin-destination travel diaries. Our bivariate analysis finds very few destinations in Montreal where a high number of trips and high percentage of trips are ending using a sustainable mode of transport and below the 15- or 30-minute travel time threshold. We further investigate the land use patterns that align with the 15- and 30-minute city to recommend realistic planning goals and policy interventions that match the North American context. The findings from this research can be of interest to transport professionals and policy makers trying to implement the 15- or 30- minute city concepts to their regions.

Flexwork and flextravel

Flexwork—flexible work arrangements that depart from the standard in-office, nine-to-five schedule—has become a staple in the post-pandemic era. The inherent flexibility of place and time in flexwork has significant implications for the travel and tourism sectors. This flexibility gives rise to adaptable travel/tourism arrangements, further blurring the boundaries between work and travel/tourism. Consequently, it creates a potential state where productivity and leisure are seamlessly interwoven. A theoretical framework is developed to encapsulate the temporality, spatiality, and liminality of this flexwork–flextravel state. This conceptualization, as a response to the increasingly indistinct demarcation of time–space dimensions and work–travel domains, carries implications for emerging travel patterns and the potential transformation of tourism landscapes and travel-related activities.

Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

With the advancement of urban modernization, more and more residents are flocking to large cities, leading to problems such as severe traffic congestion, uneven distribution of spatial resources, and deterioration of the urban environment. These challenges pose a serious threat to the coordinated development of cities. In order to better understand the travel behavior of metropolitan residents and provide valuable insights for urban planning, this study utilizes taxi trajectory data from the central areas of Beijing, Shanghai, Shenzhen, and Wuhan. First, the relationship between daytime taxi drop-off points and urban amenities is explored using Ordinary Least Squares (OLS). Subsequently, Geographically Weighted Regression (GWR) techniques were applied to identify spatial differences in these urban drivers. The results show that commonalities emerge across the four cities in the interaction between external transport stops and commercial areas. In addition, the average daily travel patterns of residents in these four cities show a trend of “three peaks and three valleys”, indicating the commonality of travel behavior. In summary, this study explores the travel characteristics of urban residents, which can help urban planners understand travel patterns more effectively. This is crucial for the strategic allocation of transport resources across regions, the promotion of sustainable urban transport, and the reduction in carbon emissions.

The travel pattern difference in dockless micro-mobility: Shared e-bikes versus shared bikes

To facilitate the tailoring of dockless bike-sharing and electric bike (e-bike) sharing services and assist in formulating effective regulations, this study aims to unravel the spatio-temporal travel patterns specific to e-bike-sharing and bike-sharing systems, utilising interpretable machine learning methods and a large-scale trip-level dataset in Kunming, China. The results show that shared bikes and e-bikes exhibit overall similarities and subtle differences in many aspects, such as trip attributes and spatial distribution. Additionally, both shared bikes and shared e-bikes have three basic temporal patterns for commuting and recreational purposes. Regarding the differences, e-bike sharing networks are more dispersed and bigger, and bike sharing tends to form densely connected clusters of flow, exhibiting a local concentration of activity. Besides, the commuting activities within e-bike sharing systems exhibit two patterns: direct travel to the destination and integration with public transit. In contrast, shared bikes predominantly rely on public transit transfers for commuting purposes.

South Korean DMZ tourists’ experience co-creation explained by motivation, interaction, and emotional solidarity

Despite the military and political tensions surrounding the Korean Demilitarized Zone (DMZ), over 1.2 million individuals visit the destination each year. Considering various tourist motivations and the group-oriented tour setting at the DMZ and employing the Interaction Ritual Theory as a theoretical guide, this study examined how South Korean DMZ tourists' motivations and interaction led to emotional solidarity, thereby facilitating the co-creation of their DMZ experience. Study results (N = 409) revealed that politics and curiosity motivations significantly influenced their interaction, while knowledge and war motivations only impacted emotional solidarity. Emotional solidarity directly contributed to experience co-creation, whereas interaction only had an indirect impact mediated through emotional solidarity. The findings enrich the understanding of the distinctive roles of South Korean DMZ tourists' motivations and highlight interaction as a key in evaluating their experience co-creation. It especially offers new insights that emotional solidarity is a critical antecedent to experience co-creation. The findings provide government officials and destination managers with practical suggestions for further management and marketing of DMZ tourism. Future research can further consider group differences when testing the conceptual model—such as DMZ tourists' travel pattern (e.g., individuals or groups) or nationality (e.g., South Korean or foreigners)—and utilize qualitative research methods to complement the quantitative findings.

From cart to door: Unravelling consumer behaviour through attitudinal sustainability profiles

In the evolving landscape of the retail industry, e-commerce has reshaped consumer behaviour, including the last mile delivery. Despite consumers typically having the final say when it comes to delivery requirements, research on last mile deliveries frequently overlooks consumer behaviour. Addressing this gap, this study explores consumer attitudes towards sustainability in last mile deliveries and assesses their impact on behavior and travel patterns. Utilizing an online survey of 2105 e-consumers, four distinct attitudinal profiles are identified —positive, neutral (due to knowledge or interest gaps), and negative attitude, shedding light on parcel weight preferences and travel patterns. The study uncovers a green attitude-behavior gap, emphasizing the need for interventions that bridge intention and action. By unraveling these complexities, this research contributes essential insights for retailers and researchers, guiding the development of targeted strategies to promote sustainable practices in the evolving landscape of e-commerce.

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

Urban day-to-day travel systems generally exist in various types of cities. Their modeling is difficult due to the uncertainty of individual travelers in micro travel decision-making. Moreover, with the advent of the information age, intelligent connected vehicles, smartphones, and other types of intelligent terminals have placed urban day-to-day travel systems in an information environment. In such an environment, the travel decision-making processes of travelers are significantly affected, making it even more difficult to give theoretical explanations for urban day-to-day travel systems. Considering that analyzing urban day-to-day travel patterns in an information environment is of great significance for governing the constantly developing and changing urban travel system and, thus, of great importance for the sustainable development of cities, this paper gives a systematic review of the theoretical research on urban day-to-day travel and its development in an information environment over the past few decades. More specifically, the basic explanation of an information environment for urban day-to-day travel is given first; subsequently, the theoretical development of micro decision-making related to individual day-to-day travelers in an information environment is discussed, and the theoretical development related to changes in urban macro traffic flow, which can be recognized as the aggregation effect formed by individual micro decision-making, is also discussed; in addition, the development of understanding different types of traffic information that travelers may obtain in an information environment is discussed; finally, some important open issues related to the deep impact of information environment on urban day-to-day travel systems that require further research are presented. These valuable research directions include using information methods to fit day-to-day travel patterns of cities and implementing macro and micro integrated modeling for urban day-to-day travel systems based on complex system dynamics and even quantum mechanics.

A reliable optimal electric Vehicle charging stations allocation

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.

Impact of pandemic restrictions on travel patterns in urban centres: A case-study of Dublin City, Ireland

The spread of Covid-19 and implementation of various restrictions changed how and why people travel. The present study analyzed three different modes of transportation, to understand the impact of Covid-19 mobility and land-use restrictions on chosen neighbourhoods in Dublin City, Ireland. Classification analysis, Spatial correlation analysis, and Bayesian change point analysis had been conducted using vehicle, cyclist & pedestrian count data from 44 neighbouring or collocated stations to explore the statistical changes in the traffic system characteristics. Apart from reduction in traffic counts, the other impacts were modal shift, rise in cyclist numbers, and similarity between weekday & weekend patterns observed. Analyses could identify that changes in the statistical aspects of traffic system are congruent with the changes in lockdown measures. Overall, this study presented a set of tools to identify the existence and degree of changes in traffic patterns over time due to any mobility or land-use policy changes.

Estimating the Size of Populations at High Risk of Malaria in 2 Operational Districts in Cambodia: Household-Based Survey.

Cambodia is targeting the elimination of malaria by 2025. The last remaining pockets of malaria in Cambodia are concentrated among populations exposed to forested areas, but the size of these populations is not well understood. To plan for the procurement and distribution of vector-control tools, chemoprophylaxis, and other commodities for malaria prevention and surveillance, robust estimates of the population at greatest risk are required. This study aims to estimate the number of forest-exposed individuals residing in Cambodia's highest-burden operational districts (ODs) in 2 provinces with active malaria transmission. In April 2023, a multistage, in-person survey was conducted among residents in the 2 ODs in Cambodia with the highest malaria burden: Sen Monorom in Mondulkiri province and Phnom Srouch in Kampong Speu province. In each OD, 10 villages were randomly selected, and 35 households were randomly selected from each village. To estimate the number of individuals at high risk of malaria-defined as residing within 1 km of a forest or traveling at least once per week to the forest-respondents were asked about the distance from their household to the nearest forested area, and their travel patterns to forested areas. To account for mobility (ie, to avoid double-counting), respondents also provided information on overnight stays at other households in the selected villages in the past month. In the 4 selected villages in Sen Monorom OD where Project BITE forest packs (an intervention in the larger research program) had been distributed prior to the survey, respondents were also asked questions to determine if they had received such a pack, to develop smaller scale "multiplier method" estimates of at-risk individuals in each of those villages. In Sen Monorom, 138 households and 872 individuals were enrolled in the survey, and in Phnom Srouch, 163 households and 844 individuals were enrolled. The estimated percentage of female householders was 49.7% (852/1716) across both ODs; the median age was 22 (IQR 12-37) years in Sen Monorom and 24.5 (IQR 16.0-40.5) years in Phnom Srouch (total age range 3-86). Based on mobility-adjusted survey estimates alone, 32% (280/706; 95% CI 19.9-47.2) of residents in Sen Monorom (an estimated 12,133-20,135 individuals) and 36% (68/198; 95% CI 24.5-45.5) of residents in Phnom Srouch (an estimated 1717-2203 individuals), met risk criteria for forest exposure. Between 125 and 186 individuals were estimated to be at risk in each of the 4 villages where the multiplier method could be applied. This study provides estimates of the number of individuals potentially at high risk for malaria infection due to forest exposure in 2 ODs in Cambodia. These estimates can support planning for malaria control and elimination efforts. The straightforward methods of household surveys and multipliers should be feasible for many national malaria control programs.

Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain

The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.

Investigating socio-spatial differentiation for metro travelers using smart card data: Older people vs. others

As population aging has been an issue worldwide, the mobility of older people have attracted the attention of scholars from urban planning, transport geography, and social science. However, few have investigated socio-spatial differentiation among mobility groups, considering their daily needs and activity spaces. To fill this research gap, we conducted a comparative analysis of socio-spatial differentiation, based on individual activity spaces. We used smart card data from Kunming, China, to identify selected individuals’ residential locations and travel patterns, and evaluate their accessed activity space. We performed a disaggregated analysis of the individual activity space, and then aggregated the activity counts on each grid. This study found that the residential locations of older metro travelers are significantly different from those of other metro travelers. In addition, socio-spatial differentiation was found to exist due to different daily requirements. The results were confirmed in three LASSO models with built environment variables. These findings are useful in urban and transportation planning to improve elder-friendly services.

Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .

Association between local amenities, travel behaviours and urban planning: A spatial analysis of a nationwide UK household panel study.

IntroductionGlobally, there has been a recent resurgence in planning policies focused on local living (such as the 20-min neighbourhood) with proposed benefits of creating sustainable and healthy places. The policies aim to provide improved spatial access to local amenities within short walkable distances of home to encourage walking and cycling and discourage car use, however these pathways have not been examined. This study aimed to quantify the density and diversity of local amenities and their association with daily/weekly travel behaviours. MethodsWe used data from Understanding Society, a national panel survey of UK adults. Spatial data were used to quantify the number and diversity of amenities that have previously been associated with active travel and focused on in 20-min neighbourhood policies within small geographical areas across the UK. These were linked to individual-level data describing daily and weekly travel behaviour for: walking (at least 10-min), cycling, car and bus use. Logistic regression models measured the association between individual amenities, their diversity and daily/weekly travel, whilst controlling for individual factors such as age, sex and employment, residing in a retail centre, as well as area-level urbanicity and deprivation. ResultsOur analysis revealed variations in active travel and car usage patterns by sex, employment status, urbanicity, and area-level deprivation. Local amenities were associated with daily travel behaviours with more amenities generally associated with more frequent walking and less regular bus and car travel. Furthermore, increased amenity diversity was associated with reduced daily car use (OR: 0.77, p < 0.001). Similar patterns were apparent for weekly travel outcomes. ConclusionsBoth access to specific local amenities as well as their diversity are both important neighbourhood factors for achieving shifts from motorised to active transport modes. Policy makers and planners need to ensure the diversity of local amenities are included in local living policies.