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Walking is the most sustainable means of daily travel for short trip distances and is a key component of the overall transport system. This paper documents variation in walking rates among countries, cities in the same country, and in different parts of the same city. Our international analysis of official government statistics shows that walking rates are highest for short trips, higher for women than for men, decline with increasing income, and remain constant as age increases. Walking fatality rates are much higher in the USA compared with the other countries we examined, both per capita and per km walked. Government policies that would increase walking rates while improving pedestrian safety include: integrated networks of safe and convenient walking infrastructure; roadways and intersections designed for the needs of pedestrians; land-use regulations that encourage mixed uses and short trip distances; lower city-wide speed limits and traffic calming in residential neighborhoods; reduced supply and increased price of parking; traffic laws that give priority to pedestrians; improved traffic education for motorists and non-motorists; tax surcharges on large personal vehicles; and strict enforcement of laws against drink and distracted driving. Five decades of success with these policies in many European cities provide practical examples for car-oriented cities to follow, especially in North America. Published version

The purpose of this thesis was to analyze the California Household Travel Survey to examine any differences in travel behavior and demographics between two of California’s multi-county Metropolitan Planning Organization (MPO) areas, the Southern California Association of Governments (SCAG) and the Metropolitan Transportation Commission (MTC). As these regions continue to grow, they have witnessed significant gentrification affecting marginalized communities that are already struggling against increasing costs of living. There were significant differences in both travel times and distance traveled with the SCAG region having values slightly higher than MTC. However, within each region there were significant differences in income and racial demographics at the county level. In SCAG, Orange County had the highest Average HH level incomes and San Bernardino and Imperial Counties having the lowest average HH level incomes. Within the MTC area African Americans and Native Americans were found to more likely walk and use public transit more than other group due to these groups having the lowest incomes out of other groups. Also, these groups tend to reside in Contra Costa and Alameda Counties which have the lowest housing costs in the MTC region.

For transportation system analysis in a new space dimension with respect to individual trips’ remaining distances, vehicle trips demand has two main components: the departure time and the trip distance. In particular, the trip distance distribution (TDD) is a direct input to the bathtub model in the new space dimension, and is a very important variable to consider in many applications, such as the development of distance-based congestion pricing strategies or mileage tax. For a good understanding of the demand pattern, both the distribution of trip initiation and trip distance should be calibrated from real data. In this paper, it is assumed that the demand pattern can be described by the joint distribution of trip distance and departure time. In other words, TDD is assumed to be time-dependent, and a calibration and validation methodology of the joint probability is proposed, based on log-likelihood maximization and the Kolmogorov–Smirnov test. The calibration method is applied to empirical for-hire vehicle trips in Chicago, and it is concluded that TDD varies more within a day than across weekdays. The hypothesis that TDD follows a negative exponential, log-normal, or Gamma distribution is rejected. However, the best fit is systematically observed for the time-dependent log-normal probability density function. In the future, other trip distributions should be considered and also non-parametric probability density estimation should be explored for a better understanding of the demand pattern.

The aim of this paper is to identify effective factors on motorcycle usage time in work-tours. To do so, a survey on motorcyclists whose workplaces are in the Central Business District (CBD) of Tehran is conducted. Due to the connected nature of daily activities, travel pattern usually has tour shape, so tour-based approach is employed in this study. For identifying and comparison of variables reflecting motorcycle usage time in simple and complex work-tours, two Accelerated Failure Time (AFT) models are developed. The results indicate that in simple tours model “home location” and “tour start time” are significant while in complex tours model “trip number”, “motorcycle age” and “driving experience” are the most significant factors. Moreover, it has been shown that trip distance and monthly fuel cost are two effective factors on motorcycle usage time in both simple and complex tours. However, trip distance has greater effect in complex tours.

Electric vehicles (EVs) offer an opportunity to dramatically decrease greenhouse gas emissions within the transport sector if fueled by renewable energy. Hawaii had been early-on considered an ideal place to launch new EVs because of the limited driving range of its island geography. Though it ranks second in new EV registrations per capita in the U.S., still the highest penetrations of EVs on the road at the zipcode level is 4%. This study identifies differences in zipcode EV registrations through 2018 using demographic and transportation behavior characteristics, as well as fuel prices and charging infrastructure. We find that income and level of education are positive and statistically significantly related to zipcode EV registrations. A zipcode with a $10,000 higher median income is associated with 8% more registered EVs, and a 1000 person increase in the population with a Bachelor's degree or higher is associated with 19% more registered EVs. While all public charging infrastructure is found to have a positive and statistically significant relationship to EV registrations, the magnitude is larger for fast charging stations (Level 3). Lastly, we find that trip distance is not linearly related to EV adoption, where zipcodes with shorter (under 20 min) and longer commute distances (45 min and more) are associated with fewer EV registrations.

Human mobility analysis is a key method for understanding urban dynamics and mobility optimization. Novel last-mile mobility, called micromo-bilities, that includes shared bicycles, electric bicycles (e-bikes), and electric scooters are seeing rapid widespread acceptance in major cities. Compared with existing mobility data such as cars, buses, and trains, the majority trip distance of micromobilities is short, typically less than a few miles. The riders use them for commuting, sightseeing, shopping, and/or fun. By using the mobility data of micromobilities, we can observe more fine-grained human mobility in urban areas than existing data sources. In this paper, we present an unsupervised learning-based technique to analyze human mobility in urban areas and to study user clusters for such micromobility services. In our approach, we cluster user mobility patterns by using non-negative tensor factorization (NTF) from area-based trip data (which only included locations of origin and destination). Our approach was applied to micromobility data collected from Chicago and Washington, D.C., and we observed characteristic patterns.

This paper describes changes in driving patterns over a five-year period of drivers aged 75 years and older (Year 1: Male = 68.9%; Age = M = 79.5 years, SD = 3.4 years, Range: 75.0–88.0 years) in the Candrive/Ozcandrive cohort study. Participants completed various functional and health assessments and self-reported driving questionnaires. In-vehicle data-loggers, installed in participants’ own vehicles, also monitored spatio-temporal characteristics of participants’ everyday driving trips. Data for a subset of one hundred and ninety-one Ozcandrive participants from Melbourne, Australia were analysed. Reductions in driving trip distance and frequency were observed over the five years. On average, in Year 1, participants drove 1223 (SD = 502) trips, totalling 8993 (SD = 5169) kilometers annually, decreasing significantly to 1028 trips (SD = 559) trips and 6787 (SD = 4624) kilometers in Year 5. On average, in Year 1, participants’ driving trips were around 7.5 km (SD = 3.2), decreasing in distance significantly to 6.9 km (SD 3.9) in Year 5. Log-normal General Estimating Equation (GEE) modelling was conducted for selected driving exposure measures (annual distance driven, annual trip frequency, trip distance, etc.). Reductions in overall annual distance driven were significantly associated with being female, increasing age, withdrawal from the study for health reasons and lower night-time driving comfort scores (marginally significant). Reductions in annual trip frequency were associated with increasing age and withdrawal from the study for health reasons. Results suggest drivers practiced self-regulation, which may reflect adaption to deterioration in health and functional status.

This paper examines bike diffusion behavior in a docked bike-sharing system in Chicago. The analysis is based on an analogy between the movement of shared bikes and the transmission of certain information on internet or the spreading of epidemics among humans. By mining a bike trip data set collected in the city, we find that (1) the distribution of bike trip distance peaks between 0.8 and 2 km, and beyond 6.3 km, it follows a strong power law; (2) the diffusion intensity of a community is affected positively by the number of incoming bike trips and rebalancing actions, and negatively by the percentage of inner-community trips. The effect of the rebalancing actions is roughly twice as strong as that of the incoming bike trips; (3) both the diffusion range of a bike and the number of rebalancing actions it receives are strong predictors of its use. Reaching one more community will produce about 14 more trips and an additional rebalancing action contributes about 8.6; and (4) even the most active bikes could only reach about 75% of all communities in Chicago. The last finding helps identify a cluster of communities poorly connected with the rest of the city by bike travel. Interestingly, these isolated communities are strongly correlated with the areas of the city that have high concentration of African American population, low-income households and homicide crimes.

Car-sharing could have substantial benefits. However, there is not enough evidence about if more people choosing car-sharing would reduce private car usage or public transport demand. This work aims to bring forward some insights by studying short-term car-sharing choice behavior. A mode choice analysis is conducted first followed by a simulation analysis to evaluate modal substitution pattern. Policy implications are obtained in terms of the possible measures that could effectively bring down private car usage. The case study is Taiyuan-China; stated and revealed preference data are collected. Mixed nested logit models are developed to study the pooled SP/RP data. The analysis is conducted separately for a shorter trip case (2–5 km) and a longer trip case (more than 5 km) to examine if results would differ by distance. It is found that raising the cost of private car usage (travel cost, parking cost) should be prioritized for shorter trips since car is more difficult to be substituted when trip distance increases. Shorter trips also need such direct measures to help suppress the demand for private car when promoting a car-sharing service; otherwise car-sharing would attract more bus users instead. Longer trips need a more effective solution to bring down private car usage and that is discovered as making car-sharing service more appealing so that it can serve as a practical substitute to private car. A number of informative indicators (e.g. willingness to pay for travel time savings, direct and cross point elasticity) are also derived to enrich the findings.

In spite of their substantial number in the U.S., our understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is sketchy. The goal of this paper is to start filling this gap for California. We perform parametric and non-parametric tests to analyze trip data from the 2012 California Household Travel Survey (CHTS) after classifying carless households as voluntarily carless, involuntarily carless, or unclassifiable based on a CHTS question that inquires why a carless household does not own any motor vehicle. We find substantial differences between our different categories of carless households. Compared to their voluntarily carless peers, involuntarily carless households travel less frequently, their trips are longer and they take more time, partly because their environment is not as well adapted to their needs. They also walk/bike less, depend more on transit, and when they travel by motor vehicle, occupancy is typically higher. Their median travel time is longer, but remarkably, it is similar for voluntarily carless and motorized households. Overall, involuntarily carless households are less mobile, which may contribute to a more isolated lifestyle with a lower degree of well-being. Compared to motorized households, carless households rely a lot less on motor vehicles and much more on transit, walking, and biking. They also take less than half as many trips and their median trip distance is less than half as short. This study is a first step toward better understanding the transportation patterns of carless households.

Following the bike-sharing system, the shared e-bike becomes increasingly popular due to the advantage in speed, trip distance, and so forth. However, limited research has investigated the impact of the introduction of shared e-bikes on the existing bike-sharing systems. This paper aims to study the effect of shared e-bikes on the traditional bike-sharing system and determine the optimal fleet deployment strategy under a bimodal transportation system. A stochastic multiperiod optimisation model is formulated to capture the demand uncertainty of travelers. The branch-and-bound algorithm is applied to solve problem. A 15-station numerical example is applied to examine the validity of the model and the effectiveness of the solution algorithm. The performance of integrated e-bike and bike-sharing system has been compared with the traditional bike-sharing system. The impacts of the charging efficiency, fleet size, and pricing strategy of e-bike-sharing system on the traditional bike-sharing system have been examined.

The rise of small businesses, self-employment, and homeworking are transforming traditional industrial ways of working Our research fills a noticeable gap in the literature by using portable devices (i e , smartphones) to capture individual mobility data on an understudied population group – small business owners (owner managers and self-employed with up to 49 employees) and whether they work from home in comparison with employees who work at their employer's premises or partly or mainly from home We recorded week-long individual GPS data on 702 participants and derived a set of measures of daily mobility (number of trips, trip duration, trip distance, and maximum distance from home) Each measure is modelled against a range of individual and neighbourhood-level covariates Our findings contrast with existing studies that suggest homeworking or self-employment may be associated with lower levels of daily mobility or with compensatory effects between work and non-work travel Overall, our study points to higher levels of daily mobility of owners of small businesses and the self-employed in cities as they travel longer distances Further, some homeworkers have on aggregate longer daily trip distances than ‘traditional’ premise-based employees Most striking, female home-based business owners fall into this group If homeworking is here to stay after the COVID-19 pandemic, we may see both increases and/or decreases of daily mobility depending on worker types and gender © 2020 Elsevier Ltd

This paper applies a data set of passenger kilometre transport demand by trip-purpose, mode type and tripdistance based on the outcomes of a National Travel Survey. The Irish Passenger Transport Emissions and Mobility (IPTEM) model enables a system-wide perspective on various measures which could be introduced to reduce passenger transport emissions. Combined with the LEAP Ireland 2050 energy systems simulation model, the carbon abatement potential of trip-purpose based policies, modal shift policies and technology improvements in public transport can be assessed. The results indicate that significant savings can be achieved from modal shift in Ireland, and that trip-purpose based targets and policies have a relevance in the effort to reduce Ireland’s energy related transport CO2 emissions. The active mode scenarios, which focus on increased walking and cycling achieve a 0.2 – 1 MTCO2 reduction in annual passenger transport emissions in 2030. The range of public transport scenarios, inspired by targets set out by the Irish Government’s Climate Action Plan achieve a between 0.001 – 0.3 MTCO2 reduction in annual passenger transport emissions in 2030. In addition, traffic camera data is used to model the impact of COVID-19 travel restrictions on transport CO2 emissions. The calculated reduction in transport CO2 emissions is 3.9 MTCO2 for 2020 and 2021. This study highlights the importance of factoring modal shift, trip distance and trip purpose into scenario analysis for transport emissions reduction, as it provides a framework for looking beyond only improving technologies in private vehicle transport.

The Internet of Things (IoT) constantly offers new opportunities and features to monitor and analyze driver behaviour through wide use of smartphones, effective data collection and Big Data analysis. In that framework, the aim of this paper is to investigate the impact of mobile phone use on driving behaviour and road safety through the investigation of driving analytics collected by smartphone sensors. For this purpose, a 100-driver naturalistic experiment was carried out and an innovative data collection scheme using a smartphone application was exploited in order to record the respective driving performance data. Statistical analyses were carried out using linear mixed binary logistic regression models in order to investigate the correlation of mobile phone use with trip characteristics, such as speed, duration, harsh events, etc. Exposure metrics found to be significantly associated with the probability of mobile phone use are total trip distance and driving on workdays and during rush hours. Additionally, the increase of the average speed is found to reduce the probability of mobile phone use while driving.

The aim of this study is to identify the factors that affect a person’s propensity to cycle in urban areas. A survey was conducted in two European cities: Dublin and Athens. Dublin boasts a substantial increase in cycling during the past years as a result of the implementation of targeted measures promoting cycling. While, in Athens the design of dedicated cycling infrastructure has commenced only recently, and Athenians’ attitudes towards cycling are still rather negative. Cycling propensity was investigated through the design of a stated preference questionnaire, in which participants were asked to state their willingness to cycle in specific scenarios, with trip purpose, trip distance and infrastructure quality being the parameters defining those scenarios. Probit models were designed and results highlighted both similarities and differences between the two sub-populations. This isolation of specific parameters defining the cycling propensity both regionally and internationally could prove important to design a future suitable transport system for each city, focused on more sustainable transport modes.

Declining active and independent mobility in primary school children poses a threat to the development of the children's psycho-motoric and cognitive abilities. Increasing accompaniment of children, which is often carried out as car transportation, creates more motorized traffic, thus leading to lower likelihood of other children traveling independently and actively. Against this background, a two-step survey was conducted to analyse the active and independent mobility (AIM) of primary school children. In a first step, mobility licenses and mobility data were collected with the help of travel diaries including specific information on travel accompaniment. In the second step, in-depth interviews with parents were conducted. Although the data can only provide a snapshot of the independent mobility of primary school children, this study confirms that active and independent mobility is the result of a variety of factors. The results indicate that – besides trip distance and age –the type of school (all-day/half-day primary school) is likewise relevant. Based on the parents' assessments, significant sharing of trips takes place in case travel accompaniment is unnecessary. Depending on the parents' attitudes towards AIM, we identified three “profiles” (Promoters, Pragmatists and Protectors). Linking these profiles to indicators of AIM reveals that different attitudes manifest themselves in children's travel patterns. The results are limited as only two schools served as sources of data and it was not possible to decouple the parental profiles from children's ages. Overall, it can be concluded that there is potential for improved active and independent mobility in children. To be effective, future campaigns need to consider parental attitudes.

This work evaluates driving behaviour, trip distance, ambient temperature, traffic condition and road grade effects on the specific energy consumption (SEC) of an electric vehicle (EV) under different operation modes according to a real driving cycle (RDC) test schedule. A compact size EV was operated in the roads of the second largest populated city in the United Kingdom for nearly four years, with real-time data collected, processed and stored by a monitoring software communicating with the electronic central unit. The trips were selected according to fully compliance with the RDC test in the individual operation modes based on vehicle speed – urban, rural and motorway – and those with shorter distances than the specifications. The driving behaviour was classified as aggressive, moderate and passive, according to dynamic operation limits, and the parameters representing the traffic conditions were stop time percentage and average vehicle speed in urban driving. The results show that the SEC is highly influenced by changes in the outside temperature, nearly doubling from operation at moderate temperatures of around 20 °C to operation at temperatures as low as 0 °C. Short trips below 16 km caused nearly 10% SEC average increase in comparison with longer ones, showing more awkward effects in motorway operation with a SEC rise up to 29%. Traffic conditions and driving behaviour also demonstrated a high influence on SEC, increasing it by as much as 40% and 16%, respectively, from the most favourable to the most unfavourable condition. In comparison with flat roads, ascending roads with 3% grade increased SEC by 50% while descending roads with −3% grade decreased SEC by 80%, with the assistance of the regenerative brake system.

Population mobility and rapid urbanization have caused migrant pupils to have commuting problems. Taking Jinjiang as an example, this research focused on the differences in school commute characteristics and mode choices between pupils from registered households and migrant households. Using two-stage (schools and students) sampling, we designed questionnaires based on pupils' school commuting patterns and collected the data for the two groups. In addition to descriptive statistics, the pupils’ choices of weekday active commuting to school were analyzed with a binary logistic model. We found that most pupils commute. Furthermore, for their weekday school commutes, most migrant pupils walk (77.48%) or commute by motorcycle (17.57%), whereas most household registered pupils walk (34.51%) or commute by private car (29.53%) or motorcycle (24.67%). Trip distance has a more significant influence on the active school commuting of household registered pupils than on the active school commuting of migrant pupils. Age significantly affects the commuting of migrant pupils only.

Acute myeloid leukemia (AML) is frequently treated with induction and consolidation chemotherapy. Consolidation chemotherapy can be delivered on an ambulatory basis, requiring some patients to travel long distances for treatment at specialized centers. We developed a shared care model where patients receive consolidation chemotherapy at a quaternary center, but post-consolidation supportive care at local hospitals. To evaluate the impact of our model on patient travel and outcomes we conducted a retrospective analysis of AML and acute promyelocytic leukemia patients receiving consolidation over four years at our quaternary center. 73 patients received post-consolidation care locally, and 344 at the quaternary center. Gender, age and cytogenetic risk did not significantly differ between groups. Shared care patients saved mean round trip distance of 146.5km±99.6 and time of 96.7min±63.4 compared to travelling to quaternary center. There was no significant difference in overall survival between groups, and no increased hazard of death for shared care patients. 30, 60, and 90day survival from start of consolidation was 98.6%, 97.2%, and 95.9% for shared care and 98.8%, 97.1%, and 95.3% for quaternary center patients. Thus, a model utilizing regional partnerships for AML post-consolidation care reduces travel burden while maintaining safety.

Traffic-related carbon dioxide (CO 2 ) emissions have become a major problem in cities. Especially, the CO 2 emissions induced by taxis account for a high proportion in total CO 2 emissions. The availability of taxi trajectory data presents new opportunities for addressing CO 2 emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO 2 emissions. This paper investigates trip-related CO 2 emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO 2 emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO 2 emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.