Daily Travel Research Articles

The effects of subsidising e-bikes on mode share and physical activity - A natural experiment

IntroductionThe promotion of increased cycling is a political goal in numerous countries due to its environmental and public health benefits. Initiatives to stimulate e-bike use, like subsidies, may be a relevant strategy. However, understanding of the effects of these subsidies remains limited. In the present study our aim was to investigate the short-term effects of subsidising e-bike purchases on mode share distribution, physical activity derived from daily travel, and overall levels of physical activity. MethodsWe used the 2020-subvention program in Oslo, Norway, as a natural experiment, employing a pre-post randomised group design. The trial group (N = 194) and control group (N = 2174) participated in a survey at two time points (in April and September 2021), which included a one-day travel diary and questions about physical activity over the previous week. ResultsOur findings revealed a significant 12.6 [7.2, 18.0] percentage point increase in bicycle mode share, followed by a decrease in the share of car and public transport usage of 10.1 [-15.9, −2.5] and 7.1 [-11.8, −2.4] percentage points. In terms of physical activity generated from daily travel, we observed a greater increase in e-cycling compared to the decrease in conventional cycling and walking, resulting in an overall rise in active transport. However, despite these findings, we did not detect a significant increase in overall physical activity resulting from this increased active mobility. ConclusionInitiatives such as subsidies for e-bikes may align with both environmental and public health goals, as the subvention led to an increase in the bicycle mode share and an overall rise in active transport. However, to assess the impact on overall physical activity, additional research utilising more precise measurements is needed.

Over 300 km Dispersion of Wild Boar during Hot Summer, from Central Poland to Ukraine.

The movement of wild boars is a complex process influenced by both internal conditions and external factors. Despite their typically sedentary lifestyle, dispersion constitutes an integral element of the wild boar's behavior. This report documents the longest observed wild boar dispersal, involving a collared two-year-old male near Warsaw, Poland. The aim of this study was to present the characteristics of movement during the "nomadic phase", drawing comparisons with the "sedentary phase". The other aim was to evaluate the influence of meteorological factors on the minimum daily travel distance of the wild boar. We collected data from two-year-old males. The first exhibited long-distance dispersal and the second only demonstrated local movements. We calculated the minimum daily distance of both wild boars based on collar locations and calculated basic statistics of movement. We used a generalized linear model with a gamma distribution and log link function to assess the potential impact of weather conditions on the minimum daily distance of wild boars. We tested maximum daily temperature, average daily temperature, and the sum of daily precipitation. The wild boar during a "nomadic phase" covered a total of 922 km with a mean minimum daily movement of 6 km. The dispersion distance was 307 km. The highest value of the minimum daily distance reached 31.8 km/day. The second wild boar (near Warsaw) covered a mean minimum daily distance of 1.4 km; the highest value of the minimum daily distance was 3.9 km. Both wild boars exhibited no dependence of minimum daily distance on weather conditions. However, when intensive and non-intensive dispersion were analyzed separately, it was demonstrated that the maximum daily temperature positively influenced the minimum daily distance. We speculate that the wild boar was forced to search for water sources after dark on hot days, which induced a longer traveling distance in an unfamiliar environment. This study highlights the significant spatial capabilities of wild boar in the transmission of genes or pathogens. We speculate that extended daily distances during the initial "nomadic phase" might suggest a panicked escape from a perceived threat. It is plausible that the wild boar found improved shelter within tall cereal crops in July and August, which resulted in lower daily distances.

Methodological Challenges for Measuring Behavioral Changes in a Longitudinal Travel Survey Under Pandemic Conditions

The outbreak of the COVID-19 pandemic has had an enormous impact on everyday life. In an attempt to stop the virus spread, unprecedented containment measures are being taken worldwide. These preventive measures are consequently affecting everyone's life and how we move. Equally remarkable is the speed at which these observed and massive changes occur and the lack of statistical evidence. In the annual cycle, the German Mobility Panel (MOP) launched an additional questionnaire for the general population, containing approximately 4,000 respondents. This paper provides insights into the MOP data quality issues and changes in daily travel under pandemic conditions.

Evaluation of a multicomponent infrastructural and app-based intervention to increase children's active school travel: A multiple methods study in Tāmaki Makaurau/Auckland, Aotearoa/New Zealand

Power Up Schools (Power Up) was a temporary intervention involving a range of activities aimed at increasing children's active school travel (AST) with a focus on physical and social aspects of schools and their surrounding environs. We evaluated the impact of Power Up on children's AST and how multiple stakeholders (children, school representatives, parents, residents) perceived the overall intervention and its components. This was a multiple methods evaluation study with four primary (elementary) schools in Auckland and their wider communities. The study involved longitudinal data collection (daily travel modes reported by children), repeat cross-sectional surveys with parents, single time-point cross-sectional surveys with residents, post intervention focus groups with children and parents, and post intervention interviews with school representatives. Findings showed evidence for increased AST; general support for Power Up by children, parents, and school representatives; mixed support by residents for some components; the importance of considering diverse needs and preferences in future interventions; the need for more comprehensive communication across stakeholders; and the value of streamlining processes for schools and parents. Comprehensive approaches that are embedded within, and accepted by, the wider school community are recommended for sustainable and effective AST interventions.

Assessing the Long-Term Impact of COVID-19 on Travel Behavior: The United Arab Emirates Perspective

The unprecedented COVID-19 pandemic has caused significant short-term travel disruptions and profound changes in daily travel routines. Yet, the long-term impact on travel behavior and mobility remains unclear. The present study examines the enduring effects of COVID-19 on travel patterns in the United Arab Emirates (UAE) through a comprehensive survey assessing long-term impacts and participants' travel behavior changes during different stages of the pandemic. The survey covered travel modes, frequency, duration, and distance, unveiling a pandemic-driven shift towards increased personal vehicle usage due to public transportation concerns. Nevertheless, as the pandemic gradually recedes, individuals are slowly reverting to their pre-pandemic travel habits. Moreover, the results indicate that travel patterns vary depending on the distance traveled. Short-distance travel has largely returned to pre-pandemic levels, while medium-distance travel continues to lag. Intriguingly, post-pandemic levels of long-distance travel have exceeded the pre-pandemic figures, indicating a potential alteration in travel behaviors. These observed changes were substantiated by a paired t-test, demonstrating a significant difference across nearly all periods examined. In summary, this research sheds light on the long-term consequences of COVID-19 on travel behavior and mobility in the UAE. The survey data uncovers a noteworthy transformation in travel patterns during the pandemic, with subsequent gradual reversion to pre-pandemic norms. Notably, the findings suggest potential shifts in travel preferences across different distances, with long-distance travel surpassing pre-pandemic levels. These insights contribute to a deeper understanding of the lasting impacts of COVID-19 on transportation and travel choices.

Willingness to pay for autonomous vehicles in China based on SEM-Logit model

Autonomous vehicles (AVs) are expected to be a new way of traveling in the future. The future popularity of AVs has a lot to do with people’s attitudes towards them. This study aims to investigate people’s willingness to pay for AVs, providing practical insights into the value of AVs in the market. Through the analysis of 1,543 questionnaires collects in China, we finds that the willingness to pay for AVs is significantly affected by psychological factors (perceived benefit, perceived safety and trust), demographic determinants (e.g., gender, age, education, income and daily travel time) and road conditions (bike lane). People who are male, younger, more educated and have higher-income are more willing to spend more money on AVs. Our study could provide recommendations for AV developers and policymakers to further promote AVs.

Urban Mobility Mode Shift to Active Transport: Sociodemographic Dependency and Potential Greenhouse Gas Emission Reduction

The aim of this study was to explore the association between travel mode choice and individual sociodemographic characteristics among urban city dwellers, as the selection of daily travel modes is influenced by several factors. The study collected 1,290 short daily trips data from 415 respondents living in Klang Valley using a random sampling technique. Logit regression models were utilized to identify the impact of sociodemographic traits on travel mode choices. Men, low education levels, students, and households without children and do not own private vehicles are more likely to choose active transportation. Besides, the study examines the potential for greenhouse gas (GHG) emission reduction. Based on Life Cycle Assessment (LCA), 142 kgCO2e or 108 kgCO2e/km of GHG were released by automobiles from the collected travel trip data. The result shows that active transport could potentially achieve 14.52% GHG emission reduction by stated preference and nearly 17% GHG emission reduction by criteria fulfillment. These findings could provide valuable information for developing practical planning policies aimed at reducing GHG emissions from the road transport sector.

Determining user specific semantics of locations extracted from trajectory data

Knowledge about people's daily travel behavior is very relevant for transportation planning, but also for urban and regional planning in general. This information is typically collected through questionnaires or surveys. With the increasing availability of mobile devices capable of using Global Navigation Satellite Systems, it is possible to derive individual mobility behavior on a large scale and for a variety of different users. However, the challenge is to derive the relevant information from the mere GNSS trajectories; in this paper, the relevant information is semantic locations such as home, work place or leisure places. This paper presents an approach to first detect and cluster stop points as potential semantic locations of a user, which are then enriched with Points of Interest from OpenStreetMap and additional features, and finally a Viterbi optimization assigns the most probable semantics to these locations. Overall, this approach produces promising results for predicting user location semantics on a generalized level.

Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic.

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.

Heterogeneity in users’ intention-to-use Urban Air Mobility services

Urban Air Mobility (UAM) is receiving increasing attention by the industry and the scientific community. The first commercial services are expected to be launched in a short period. However, users’ acceptance and willingness-to-use UAM should not be taken for granted and need to be carefully assessed. This study presents the results of a large-scale RP-SI (Revealed Preference-Stated Intention) survey carried out in the metropolitan area of Milan (Italy): it was designed to collect data aiming at both profiling potential UAM passengers and developing models to assess the impact of different factors on users’ intention-to-use UAM services. The results obtained through hybrid ordered choice modeling analysis outlined the statistical importance of three identified latent constructs (i.e., flying concerns, propension towards technology and UAM safety concerns) in explaining the intention of using UAM services. Particularly, concerns regarding the flight or the UAM safety have been found to have a minor negative influence in the intention to use UAM compared to the user's propension towards technology which has instead a major positive influence. Moreover, a lower propensity towards embracing these new aerial services has been found in females and in those currently not traveling alone, while those traveling for business purposes or using taxis for daily travels exhibit a higher propensity towards their adoption.

Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis

Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events.

Socio-economic and demographic differences in the impact of COVID-19 on personal travel in the Global South

ABSTRACT This paper presents the results of a scoping review concerning the state of knowledge with respect to the impacts of COVID-19 on daily personal travel in the Global South. Based on the available literature in the Global South, the paper aims to: (1) provide an overview of the current state of knowledge regarding the personal daily travel of different socio-economic and demographic groups during COVID-19; (2) synthesise the literature to explore the needs of the different socio-economic and demographic groups; and (3) identify groups who received less attention in transportation research in the Global South so far. The paper reviewed 47 studies and found that while investigating personal travel during COVID-19, the most explored socio-economic and demographic attributes were sex, age, income, occupation and educational qualifications. Some regional differences were evident in terms of mode choice during COVID-19. Through the review, it is also noticeable that none of the studies explored LGBTQ+ communities’ and individuals with disabilities’ transportation needs and challenges and how COVID-19 has impacted their personal travel. Other overlooked socio-economic and demographic groups in the Global South whose personal travel during COVID-19 and the post-pandemic period needs investigation are migrant and seasonal workers, children and youths, ethnic minorities, racial minorities, religious minorities, linguistically diverse individuals, indigenous individuals, and individuals residing in rural areas.

Outdoor Navigation Assistive System Based on Robust and Real-Time Visual-Auditory Substitution Approach.

Blindness affects millions of people worldwide, leading to difficulties in daily travel and a loss of independence due to a lack of spatial information. This article proposes a new navigation aid to help people with severe blindness reach their destination. Blind people are guided by a short 3D spatialised sound that indicates the target point to follow. This sound is combined with other sonified information on potential obstacles in the vicinity. The proposed system is based on inertial sensors, GPS data, and the cartographic knowledge of pedestrian paths to define the trajectory. In addition, visual clues are used to refine the trajectory with ground floor information and obstacle information using a camera to provide 3D spatial information. The proposed method is based on a deep learning approach. The different neural networks used in this approach are evaluated on datasets that regroup navigations from pedestrians' point-of-view. This method achieves low latency and real-time processing without relying on remote connections, instead using a low-power embedded GPU target and a multithreaded approach for video processing, sound generation, and acquisition. This system could significantly improve the quality of life and autonomy of blind people, allowing them to reliably and efficiently navigate in their environment.

Short-term Passenger Flow Prediction of Urban Rail Transit based on ARIMA Model

Urban rail transit is very important in modern urban transportation, which meets the needs of People's Daily travel. This paper aims to introduce the ARIMA model into the short-term passenger flow forecast of urban rail transit, analyze its principle and application method, and build ARIMA model combined with AFC data to forecast short-term passenger flow of urban rail transit. Through the research and improvement of ARIMA model, it is anticipated that it would give urban rail transport operators more precise and trustworthy short-term passenger flow forecasting capabilities, so as to realize more intelligent and efficient operation of urban rail transit system. In this study, the ARIMA model was established, and rigorous scientific methods such as residual test, autocorrelation function (ACF) and partial autocorrelation function (PACF) test, and comparison of predicted value and real value were adopted to conduct tests step by step according to the experimental process. Finally, it was confirmed that the excellent prediction accuracy of the ARIMA prediction model used in this paper, can provide accurate prediction results, and can be used in practical scenarios.

Simulating the performance of HOV lanes for optimal urban traffic management

Traffic congestion is a significant problem in crowded cities, resulting in increased travel times, air pollution, and reduced productivity. High-occupancy vehicle (HOV) lanes have emerged as a promising solution to tackle these issues. HOV lanes are reserved for vehicles carrying multiple passengers, incentivizing shared travel and reducing the number of low-occupancy vehicles on the road like school buses. By reducing the overall number of cars on the road, HOV lanes can significantly alleviate traffic congestion, improve travel times, and reduce air pollution. The effectiveness of HOV lanes depends on the degree of demand for shared travel and the availability of incentives, such as employer-provided benefits or toll exemptions. Additionally, the design and implementation of HOV lanes must be carefully planned to ensure that they do not negatively impact other road users. Tehran, as one of the largest and most populous cities in Asia, is a major destination for daily travel by workers. The Tehran-Karaj freeway serves as the primary and busiest entrance to Tehran city, experiencing heavy traffic every day in the morning towards Tehran and in the evening towards Karaj. In this study, the traffic conditions of this freeway were simulated in the existing state and with the presence of HOV lanes, and the resulting traffic parameters were compared and analyzed under different traffic scenarios. The findings of this research can offer valuable insights for decision-makers and traffic policy makers.

Changes in wintertime visibility across China over 2013–2019 and the drivers: A comprehensive assessment using machine learning method

Effective emission reduction measures have largely lowered the particulate concentration in China, but low-visibility events still occur frequently, greatly affecting people's daily life, travel, and health. In the context of carbon neutrality strategy and climate change, the mechanisms governing visibility changes may be undergoing a transformation. To address this critical issue, we have undertaken a comprehensive assessment by employing a novel approach that combines site observations, model-derived datasets, and machine learning techniques. Our analysis of the dataset shows varying degrees of improvement in wintertime visibility in regions such as North China, South China, and the Fenwei Plain over 2013–2019, but an unexpected deterioration (approximately 1 km yr−1) in central and southern China (CSC). We further elucidate key roles of PM2.5 reduction in these regions with visibility improvement; whereas the unsatisfactory visibility trend in CSC was caused by combined effect of relative humidity (RH) increase (47 %), aerosol hygroscopicity (κ) enhancement (9 %), and boundary layer (BLH) reduction (8 %), which greatly overwhelms the effect of PM2.5 reduction recently. Moreover, the study reveals a growing influence of RH on the wintertime visibility, reaching 40 % ± 24 % to the total contribution in 2019, while that of PM2.5 declined to 18 % ± 19 % and is expected to further diminish with emission reduction. Note those often-neglected factors-temperature, wind speed, BLH, and κ, account for over 40 % of the total contribution. Though the importance of aerosol hygroscopic growth to visibility was found decreasing recently, it retains non-negligible impacts on driving inter-annual visibility trends. This study yields innovative insights for air pollution control, underscoring the imperative of region-specific strategies to mitigate low-visibility events.

Road Rage and Anger-Provoking Situations in Sulaimani City- Iraq

Driver behavior factors, including road rage factor, are often responsible for increasing traffic accidents that result in deaths, physical injuries, psychological problems, and financial losses. In order to improve traffic safety problems in our country, road rage issues related to anger-provoking situations were explored. To collect data, two questionnaire survey forms related to road rage level, traffic characteristics, and demographics of the drivers were prepared. The road rage questionnaire form included 12 questions related to popular anger-provoking situations. Six Likert scales were scored starting from 0% to 100%. Out of 600 spread forms, 463 valid forms were accepted to be analyzed. Different participants having different gender, age, education level, driving experience, vehicle type, daily travel time, vehicles’ price, traffic accidents, and the road rage history were contributed. A statistical software program (SPSS 26) was utilized to analyze the data. It was obtained that there are significant correlations among road rage-provoking anger questions and the total overall rate of the road rage. On the other hand, the results showed that male drivers are angrier than female drivers during driving. The older the drivers, the less overall road rage values were obtained. The overall road rage value decreased as the education level of the drivers raised to higher level. Driver skills had significant effects on the results of the overall road rage value of the drivers in which the drivers having 4 to 6 years of driving license were angrier than the other drivers, while drivers having driving license more than 6 years had the less feeling of anger during driving. Regarding types of vehicles, the bus, taxi, and then truck drivers had more overall road rage values than PC and SUV drivers. As the drivers traveled more distances, the obtained results of the overall road rage values became greater. The drivers who have high-price vehicles were angrier than those have low-price vehicles. The Drivers’ accident history did not significantly relate to overall road rage values. In addition, the drivers who did not have any road rage history in their real life had the smallest overall road rage values; while those drivers faced physical fighting and engaged police stations due to road rage problems had the greatest overall road rage value.

Microgrids for green hydrogen production for fuel cell buses – A techno-economic analysis for Fiji

The study examines the feasibility of producing hydrogen for fuel cell buses in Fiji. The paper focuses on sizing hybrid microgrids comprising solar panels and wind turbines as the primary power source for hydrogen production while considering both off-grid and grid-connected cases. As no fuel cell vehicles exist in Fiji at present, five long-distance buses with daily travel of 380 km are proposed as a pilot scale baseline system, which would operate in the daytime only. Detailed technical and economic modelling was done using the HOMER Pro software with cost inputs derived from literature. A total of three cases for the on-grid and four cases for the off-grid systems are analyzed based on the various combinations of components. HOMER Pro optimized the grid configurations to minimize the Net Present Cost (NPC) values. All seven cases were also modelled with and without the effects of hydrogen gas compressors to emulate the production of low-pressure gas without compression and compressed high-pressure hydrogen required for fuel cell buses. It was found that a grid-connected system with solar and wind hybrid was the most feasible configuration and yielded the lowest NPC of $6 million. The grid-connected case also yielded a Levelized Cost of Electricity (LCOE) of $0.10/kWh and a Levelized Cost of Hydrogen (LCOH) of $9.08/kg with compression to 400 bars. The LCOH stood at $8.73/kg for hydrogen generation without compression. The most feasible off-grid configuration using solar and wind power attained an LCOE of $1.15/kWh while giving a LCOH of $13.00/kg for compressed hydrogen. It was also found that neglecting hydrogen compression load and costs can underestimate the LCOH by as much as 6.92 %. The off-grid best case had no CO2 produced during its operation, leading to a net zero process that offsets 929.9 tons of CO2 compared to the baseline. The grid-connected system does have some emissions but leads to a 93.9 % reduction in CO2 emissions, offsetting 873.3 tons of CO2 compared to the baseline. The study updated the cost inputs to account for inflation, logistics, and hydrogen gas compression, which are not available directly in HOMER Pro software. At the time of writing this paper, no feasibility study existed for hydrogen production in Fiji using renewable energy.

Varying mobility adaptation to COVID-19 of residents in urban villages and commodity housing communities: A quasi-experimental analysis in Shenzhen, China

Although studies have explored the travel behavior of residents in informal housing communities in recent years, few have been conducted in the context of a time series before and after the outbreak of COVID-19. This paper tracks individual-level daily mobility response for three months, using an interrupted time series (ITS) quasi-experimental design to infer daily causal effects of COVID-19 and community close-off management on individuals' daily travel distances. This study compares the mobility response to COVID-19 between people living in urban villages and commodity housing communities in Shenzhen, China. The results reveal that urban village residents reduced their travel less, with slower response and recovery rates after the COVID-19 outbreak. The community close-off management was less effective for urban village residents, with a smaller abrupt reduction in mobility and their recovery rate increasing faster during the post-lockdown period. Therefore, urban village residents may have experienced greater limitations in adjusting travel with higher exposure risks during the pandemic, compared with commodity housing residents. The variations in mobility change are significantly affected by some inherent differences between these two groups, even after controlling for their socioeconomic and built environment features. These results provide important insights for designing mobility restriction policies for different housing groups and planning more resilient neighborhoods in the post-COVID era.

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.