Travel Patterns Research Articles

Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future.

The Influence of the COVID-19 Pandemic on Travel Behaviour in the Greater Copenhagen Area

The COVID-19 pandemic drastically changed the way of living for billions of people and severe restrictions were implemented by governments around the world, affecting the travel patterns of all citizens. This article investigates how travel patterns changed in the Greater Copenhagen area of Denmark during the full two-year period covering 2020 and 2021, thus allowing for an analysis of both the short-term and medium-term impacts as society gradually reopened and restrictions were lifted. The analysis covers large-scale travel survey data as well as a segmentation clustering analysis of public transport smart card data. The results showed that impacts were strongly linked to changes in trip purpose and were thus not uniformly distributed throughout the public transport system. User segmentation analysis revealed that most users changed to less intense travel use of public transport. The results highlight important policy implications in terms of how to adapt service provision within a public transport network more efficiently.

Demand Prediction and Influencing Factors Analysis of Shared Bicycles near Public Transportation Stations based on GCN

Traffic prediction is a pivotal technology in the development of intelligent transportation systems. Real-time and accurate traffic prediction holds significance in route planning, resource allocation, and improving travel efficiency. To enhance the convenience of shared bicycle travel near public transportation hubs and optimize shared bicycle deployment strategies, this paper proposes the utilization of a graph convolutional neural network prediction model. Initially, shared bicycle travel data is scrutinized for spatiotemporal characteristics. Subsequently, an adjacency matrix is constructed based on the spatial attributes of the data to establish the graph convolutional network. Lastly, global spatial autocorrelation analysis and a coupled coordination model are integrated for validation and augmentation. This model is employed to predict shared bicycle travel patterns near public transportation hubs in the Xiamen Island area. Experimental results demonstrate a high level of precision, underscoring the model's effectiveness in providing valuable guidance for shared bicycle travel. The effectiveness of this method can be further verified in subsequent empirical studies.

Revealing the mobile sensing powers of semi‐random and deterministic “drive‐by” vehicle fleets

AbstractThe utilization of mobile sensing through the deployment of sensors on third‐party vehicles that pass by an area offers notable benefits in terms of spatiotemporal coverage, precision, timeliness, and cost‐effectiveness for monitoring urban environments. However, the diverse travel behaviors exhibited by different types of vehicle fleets result in varying levels of sensing powers. Drawing inspiration from these phenomena, this study quantifies and compares the travel patterns and sensing powers of bus and taxi fleets. The experimental findings unveiled several noteworthy laws, such as a diminishing rate of increase in sensing power with the stable increasing vehicle number, the high sensing power exhibited by taxis, the comparable spatial coverage between buses, and the relatively low uncertainty associated with buses' sensing power. Furthermore, the impact of travel patterns (i.e., semi‐random behavior of taxis and deterministic behavior of buses) on the powers of mobile sensing was revealed. The aforementioned findings provide valuable insights for the development of efficient mobile sensing schemes and hold significant relevance for the implementation of smart city infrastructure.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.

Recent Latinx immigrants to Miami/Dade County (FL): Travel patterns before, during, and one year after the COVID-19 pandemic lockdown

Introduction: Understanding the transportation needs of immigrants is crucial for the design and promotion of safe, equitable, and sustainable living environments. This study examines the transportation patterns from a sample of Recent Latinx Immigrants (RLIs) upon arrival to Miami/Dade Co (MDC), Florida. Methods: Collected between 2018 and 2021, data came from a longitudinal study examining drinking and driving trajectories among 540 RLIs to MDC. Retrospective pre-immigration data (T0) were obtained simultaneously with the first-year post-immigration data (T1). Follow up surveys were conducted one year later, before (T2-BC) or during a pandemic lockout (T2-DC), and two years later (T3). Descriptive and repeated measures mixed-model regression were used to examine the data. Results: Driving declined from T0 to T1, although remained higher than previously reported for other locations. Not having a valid driver’s license was the main reason for the decline. The initial reduction in driving was paralleled by an increase in the use of transit, riding as passengers in private vehicles, and walking. A year later (T2), as RLIs’ income and access to a driver’s license grew, driving rates increased (even during the pandemic lockdown), while the use of other transportation modes decreased. A year after the pandemic lockdown (T3), driving as well as the use of other transportation modes receded. Reasons for this decline are unclear. Conclusions: RLIs reported elevated driving rates upon their arrival to MDC. The COVID-19 pandemic seems to have altered the RLIs’ transportation patterns, provoking an overall decline in mobility that lasted even after the pandemic lockdown ceased. Practical applications: Transportation planners working on developing safe and equitable transportation systems in MDC should: (1) identify and address barriers to the use of transportation modes other than driving by RLIs; and (2) understand reasons for the broad decline in transportation modes after the pandemic lockdown.

Unique Nature of Cilember Curug Megamendung as a Main Destination

This research discusses the uniqueness of the tourist destination at Curug Cilember as a mainstay destination. Cilember Waterfall is located in Megamendung, Cilember Village, Cisarua District, Bogor Regency. Officially operating since 2000, Curug Cilember Megamendung has an attraction in the form of seven beautiful and natural waterfalls, with the height of the waterfall, the width of the water flow, the beauty of the surrounding scenery, various recreational activities that can be done with the family, ease of transportation and travel routes. to be accessed by private vehicles and public transportation, so it can be indicated as a mainstay destination. The research methods used include direct observation, interviews with local people, and document analysis. A qualitative descriptive approach was used to fully understand the natural beauty of Curug Cilember. The aim of this research is to identify the uniqueness and other potential of Curug Cilember Megamendung including biodiversity, surrounding environment and attractiveness. The research results show that all the beauty of Curug Cilember is able to make it a mainstay destination and can be perfected again with a tour package and overcoming challenges that may occur in managing travel patterns to maximize opportunities for tourist comfort, making Curug Cilember a mainstay destination that must be visited.

Synapse formation and plasticity by cell adhesion molecules cadherin and nectin

Synapses are sticky structures that physically connect neurons to one another as well as locations of cell-to-cell communication generated between the axons and dendrites of neurons. Synapse formation is hypothesized to be facilitated by a number of molecular processes, including cadherins and nectins. Calcium is necessary for the cadherin cell adhesion molecule. It has been shown that cadherins and catenins localize between axons and dendrites in neurons from the earliest stages of synaptogenesis. Cadherins affect how synapses form and change throughout time. In addition to cadherins, nectins are known to contribute to the construction and upkeep of intercellular adhesions. The brain expresses nectin-1,2 and 3. Here, the author reviews the specific effects of N-cadherin deletion on synapses and its relationship to diseases. Additionally, it has been discovered that nectin-1 and 3 play physiological roles in the development of synapses, alteration of mossy fibers' travel patterns, inter-neurite affinity, acquisition of contextual fear memory, and stress-related mental diseases. Nectin-2 has critical roles in the establishment of synapses as well as the homeostasis of astrocytes and neurons in the particular areas of the brain where it is generated. Additionally, a human NECTIN2 gene variant has been associated with Alzheimer's disease.

Cyber Physical System: The Role of Website as a Medium of Digital Marketing for Ecotourism

The advancement of human civilisation and the development of the global economy in various fields has now entered Indonesia with various new digital-based technologies and is currently known as the industrial era 4.0 which is based on cyber physical systems. Digitalisation in various fields is currently also intensively carried out in the tourism sector which is one of the country's foreign exchange boosters. Indonesia's tourism development is supported by adequate human resources and the diversity of natural resources which is an added value in order to sedate tourists to visit tourist destinations in Indonesia. The tourism village will later become an independent tourism village with sustainable tourism village certification. The assistance is carried out through the development of Human Resources in the management of tourist villages, product development or travel patterns, and digital development. In the management of digitalisation-based Independent Tourism Villages, the 7 destinations have used websites as a marketing communication medium. With the existence of digital technology-based marketing communication, many Tourism Villages are now increasingly recognised by the wider community across time and space. Marketing communication through websites is one of the right strategies for tourism businesses because it is a more efficient and effective media display and has a positive significance for the tourism sector.

Travellers’ Attitude and Intention towards post Covid-19 Travel: The Moderating Role of Traveller Personality Type

COVID-19 has crucially changed the motivations, attitudes, and behaviours of travellers. This study explores the shifts in travel attitudes after the COVID-19 pandemic and how these changes affect the intention for upcoming travels. It investigates the moderating role of the traveller personality in forming the relationships between travel attitude’s antecedents and future travel intention. The study adopts the deductive approach and employs the quantitative method to achieve its aim. It compares the perceptions collected via a questionnaire from random travellers from three countries: UAE, Egypt, and Jordan. PLS-SEM is used for data analysis. It is revealed that protection motivation intentions, destination selection factors, and travel patterns/ arrangements predict travel attitudes post the pandemic. Cautious travellers are highly concerned with protection intentions and destination selection post-epidemic compared with normal travellers. Findings help us understand the shifts in travel attitudes and intentions after the pandemic. Understating the influence of the epidemic on tourists’ attitudes and intention to travel post-pandemic is strongly needed to accelerate tourism recovery and ensure a safe travel environment for tourists. Theoretically, the study responds to the research calls for examining the changes in travel motivations, attitudes, and behaviours. Practically, the study profiles travellers based on their travel personality (i.e., cautious versus normal travellers) and identifies the characteristics of each category. This will help destination marketers and service providers to adopt the relevant strategies to meet the tourists’ needs, expectations and fears in the post-pandemic new normal.

Exploring the Resilience of Public Transport Trips in the Face of Urban Violence from a Gender Perspective

Public transport systems that ensure safe and efficient mobility are essential to promote sustainability in cities. However, public transport is susceptible to violence. Additionally, men and women have distinct perceptions of security, which can lead to different reactions in the face of danger. Therefore, considering this situation from a gender perspective, the goal of this study is to assess the levels of resilience in trips made by public transport users when exposed to violence. Data were collected from 763 individuals (women: 60.8%; men: 39.2%) within the academic community of a university campus in Rio de Janeiro, Brazil, through an online questionnaire. The information obtained included the participants’ socioeconomic details, security perceptions, and changes in travel patterns due to security concerns. The results of the Cronbach’s alpha test (0.842) indicated a good internal consistency within the data. Chi-squared tests of independence were applied, and calculations for effect size measures were conducted to evaluate the possible association between gender and other variables. Regarding the level of resilience of the trips made, evidence was found that the perception of safety and the behavior of public transport users are influenced by their gender. Regarding the gender of the respondents, an association was found with the resilience levels of trips. Furthermore, it was found that women are more vulnerable to violence in public transport than men, with risks of feeling unsafe during walks to or from stations and on buses being 1.1 and 1.5 times higher, respectively. Additionally, it was observed that the behavior of public transport users is significantly influenced by past victimization experiences, prompting measures for greater protection to be sought. The results of this study allow for a better understanding of how men and women feel exposed to violence when using public transport and can contribute to the creation of public policies to promote safety. Additionally, they can assist security authorities in directing and concentrating police efforts more effectively.

Impacts of public transit delays and disruptions on equity seeking groups in Toronto – A time-expanded graph approach

This study employed graph theory techniques to evaluate the global efficiency of Toronto's transit network. Using global efficiency, which measures the ease with which riders could reach their destinations, we explored if disadvantaged riders suffered the same level of travel time delays as the general population during both minor (recurrent) service disturbances and major (non-recurrent) disruptions. We modified the global efficiency measure by applying weights representing origin-destination trip flows for six population groups of interest separately. We used a time-expanded graph, which we built using real-time data scraped from the public APIs of the Toronto Transit Commission. This approach allowed us to create travel time estimates that considered the effects of real-world headway performance on travel times over 16 days during the winter of 2019. Our analysis found that disadvantaged groups experienced similar reductions in global efficiency and similar increases in travel time during both routine delays and major transit disruptions compared to the general population. Groups with predominantly suburban travel patterns, such as Black and Low-Income riders, had slightly lower drops in global efficiency than the general population, showing their better resilience to delays and disruption.

The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

COMPARING THE SPATIOTEMPORAL TRAVEL PATTERNS AND INFLUENCING FACTORS OF BIKE SHARING AND E-BIKE SHARING SYSTEMS

Abstract. The emergence of micromobility, exemplified by bike sharing and e-bike sharing systems, has ushered in a low-carbon, environmentally friendly, and sustainable revolution in urban transportation. This transformative shift addresses the "first and last mile" challenge and holds immense potential for urban mobility enhancement. Nevertheless, the existing literature predominantly investigates the spatiotemporal travel patterns and influencing factors of bike sharing and e-bike sharing systems in isolation, overlooking comparative analyses grounded in quantitative methodologies. In order to fill this gap, this study first compares and analysis their spatiotemporal travel patterns, which are measured by travel distance, travel time, and travel volume. A Multiscale Geoweighted Regression (MGWR) model was constructed using various data sources, such as Point of Interest (POI) data, metro station data, and bus stop data, to conduct a spatiotemporal correlation analysis of land use and public transport factors with the travel volume of the shared system. Our study centers on Manhattan, New York City, utilizing data from May 2022 for both bike sharing and e-bike sharing systems. The study analysis reveals that hourly trip volumes are higher for bike sharing than for e-bike sharing, exhibiting substantial spatial variation across different regions within the city. The MGWR model's findings suggest that educational facilities exert a negative influence on bike sharing in the northeast and on e-bike sharing trips in the central region, with this impact being more pronounced on weekends. Similarly, cultural facilities negatively affect the Central region's bike sharing system and the citywide e-bike sharing system, with a milder effect during weekends. Moreover, bus stops exhibit a significant negative impact on bike sharing and e-bike sharing at Chelsea Waterside Park (only weekdays), while displaying a positive influence on both systems during weekends. To validate the MGWR model's efficacy, we conducted a comparative analysis with a Geographically Weighted Regression (GWR) model. The results demonstrate that MGWR can be more effective in correlating and quantitatively explaining the effects of different factors on spatiotemporal travel patterns. In conclusion, this study furnishes valuable insights for optimizing urban infrastructure rebalancing strategies and advancing sustainable urban infrastructure development.

Physics-based modeling of electricity load profile of commercial building stock considering building system composition and occupancy profile

Physics-based building stock energy models (BSEMs), robust support tools for policymaking, play an important role in modeling time sequence electricity load profiles of the commercial sector. However, the conventional modeling approach, assisted by physics-based BSEM, inadequately considers the occupancy profiles of large populations and the heterogeneity of building systems, which could result in significant errors. To address this issue, this study proposes a novel framework of physics-based BSEM for modeling electricity load profiles in the commercial sector. The proposed framework incorporated both occupancy modeling, using travel survey data on daily travel patterns of the population, and stock modeling, to consider the heterogeneity of building systems within the physics-based BSEM. The methodology provided that (1) the reproducibility of occupancy presence schedules and electricity load profiles can be enhanced by the occupancy modeling; (2) building system stock modeling can improve the estimated electricity load profile. Moreover, the suggested method offers analytical capabilities for forecasting long-term variations in electricity load profiles and estimating the demand response potential of commercial building stocks.

Analysing the effect of COVID-19 on the localness of visitors to Florida state parks and New York attractions using online reviews, tweets, and SafeGraph travel patterns

ABSTRACT Background This study assesses the impact of COVID-19 on U.S. domestic tourist travel to selected Florida state parks and two New York City (NYC) attractions, inferred from user-generated content (UGC), i.e. TripAdvisor reviews, Yelp reviews, and tweets, as well as SafeGraph Point of Interest (POI) visitation patterns. More specifically, it determines the effect of the pandemic on the localness of POI reviewers and visitors, measured by the distance between home and POI, as well as the proportion of reviewer/visitor home regions that fall within the same state as the POI. Results Results show a shift towards more local reviewers and visitors of POIs in both study areas during the pandemic, which is statistically significant for all four data sources for POIs in NYC, but only for SafeGraph and a subset of UGC platforms for Florida state parks. Conclusions This shows that the power of UGC-based analysis of travel pattern change in rural regions is limited by data scarcity compared to more populated areas and that SafeGraph visitation patterns provide a reliable data source for such a purpose even for POIs in rural settings.

Emerging bacterial and fungal pathogens in healthcare and the threat of drug resistance

Managing the treatment of fungal infections, in healthcare facilities is extremely important as drug resistance continues to rise. It's crucial to have programs that oversee the use of antibiotics which can help reduce the emergence of resistance. Rapid diagnostic tools like polymerase chain reaction and next generation sequencing (PCR and NGS) are helpful in targeting treatment plans. Implementing rigorous infection control protocols can effectively reduce the transmission of infections, within healthcare facilities. Collaborative efforts between specialists ensure customized treatment plans that address resistance profiles. Combating evolving drug resistance involves approaches such as combination therapy and immunotherapies. Educating healthcare workers and patients about infection prevention and control is essential. Surveillance and epidemiology play a role in tracking resistance patterns, which in turn guide interventions. International cooperation is crucial due to travel patterns. Continuous research and innovation are essential for management providing insights into treatment effectiveness and mechanisms of resistance. The clinical implications of drug resistance such as increased severity, treatment failures and limited options underscore the need, for an approach. In conclusion, it is imperative to have clinical management strategies to tackle the challenges posed by emerging drug pathogens while safeguarding patient outcomes and public health.

KARAKTERISTIK PENGUNJUNG OBJEK WISATA PIUGUS RESORT DESA BELIBAK DI KEPULAUAN ANAMBAS RIAU

Belibak is a village in Palmatak District, Anambas Islands Regency, Riau Islands Province. Belibak was designated as a "new tourism" village by Mr. Abdul Haris, the regent of Anambas Regency in 2015. Among those small islands, Anambas has five big separate lands that could be new tourism potential, namely Belibak, Piugus, Pidik, Mantang Besar and Mantang Kecil. Piugus Resort in Belibak village is known for its calm and quiet beach which attracts tourists to come to visit. In this research the author analyzes the characteristics of visitors to the Piugus Resort tourist attraction in Belibak village. The atmosphere of Piugus Resort, which is not as busy as other tourist attractions, encourage this research to be carried out. This research aims to determine the characteristics of visitors based on the characteristics of travel patterns. The method used in this research is a qualitative descriptive method and data was collected using a direct interview method with the management of Piugus Resort. The results of this research show that tourists visiting Piugus Resort have varying characteristics. In terms of destination, tourists who come to visit Piugus Resort have the aim of vacationing and resting. Visitors who come to Piugus Resort are mostly foreign tourists. The travel to get to Piugus Resort is five minutes by speedboat from Belibak village or 15 minutes by sea-motorbike. In term of length of stay at Piugus Resort is more than 24 hours to a month's stay. Most tourists who come to visit Piugus Resort to stay overnight, travel and spend holidays together, especially with colleagues or with family.

Analysis and numerical simulations of travelling waves due to plant–soil negative feedback

Abstract In this work, we carry out an analytical and numerical investigation of travelling waves representing arced vegetation patterns on sloped terrains. These patterns are reported to appear also in ecosystems which are not water deprived; therefore, we study the hypothesis that their appearance is due to plant–soil negative feedback, namely due to biomass-(auto)toxicity interactions. To this aim, we introduce a reaction-diffusion-advection model describing the dynamics of vegetation biomass and toxicity which includes the effect of sloped terrains on the spatial distribution of these variables. Our analytical investigation shows the absence of Turing patterns, whereas travelling waves (moving uphill in the slope direction) emerge. Investigating the corresponding dispersion relation, we provide an analytic expression for the asymptotic speed of the wave. Numerical simulations not only just confirm this analytical quantity but also reveal the impact of toxicity on the structure of the emerging travelling pattern. Our analysis represents a further step in understanding the mechanisms behind relevant plants‘ spatial distributions observed in real life. In particular, since vegetation patterns (both stationary and transient) are known to play a crucial role in determining the underlying ecosystems’ resilience, the framework presented here allows us to better understand the emergence of such structures to a larger variety of ecological scenarios and hence improve the relative strategies to ensure the ecosystems’ resilience.

Comparative Analysis of Usage Patterns and Underlying Determinants for Ride-hailing and Traditional Taxi Services: A Chicago Case Study

As app-based ride-hailing (or e-hailing) services have achieved great success worldwide, the traditional taxi industry faces an unprecedented crisis. To gain a deeper understanding of these two distinct modes that both offer door-to-door mobility services, in this study, we use Chicago as a case to comparatively analyze the travel patterns of the app-based ride-hailing and traditional taxi services and understand how socio-demographic and urban land use attributes affect the usage of two services in temporal and spatial dimensions. We employ the local regression models named geographically and temporally weighted regression (GTWR) models that can capture the spatiotemporal nonstationarity between variables as the primary analysis tool. Four GTWR models that predict the average hourly taxi or ride-hailing trip volume on weekdays or weekends for each community area are carefully constructed with R^2 all above 0.98. The nonstationarity test shows all variables exhibit extra local variations, indicating the necessity of using the GTWR models to explore the attributes’ spatiotemporal impacts. Spatial error models (SEM) and ordinary least square (OLS) models, global regression models, are also built as comparisons and benchmark references. The differences between global and local regression models are illustrated through clustering analysis. To compare and contrast the usage patterns of ride-hailing and taxi services, we analyze the temporal-spatially varying coefficients of GTWR models. Interpretations are provided for our interesting findings, including the strong trip generation power of the population in the South Side area, the attitude of white people towards taxi services is positive but ambiguous towards ride-hailing services, young people's opposing behaviors towards taxi services on weekdays and weekends, and the opposite relationship between transit and taxi or ride-hailing in certain areas. Based on these key findings, we offer some planning and operational suggestions, such as developing mobile apps, implementing price-related strategies, strengthening partnerships with local businesses, building multi-modal transportation, clarifying customer segmentation, providing auxiliary services, etc.