Academic literature on the topic 'Non-Commuting travels'

The reduction in locational traffic accident risks through appropriate traffic safety management is important to support, maintain, and improve children’s safe and independent mobility. This study proposes and verifies a method to evaluate the risk of elementary school students-vehicle accidents (ESSVAs) at individual intersections on residential roads in Toyohashi city, Japan, considering the difference in travel purposes (i.e., school commuting purpose; SCP or non-school commuting purpose: NSCP), based on a statistical regression model and Empirical Bayes (EB) estimation. The results showed that the ESSVA risk of children’s travel in SCP is lower than that in NSCP, and not only ESSVAs in SCP but also most ESSVAs in NSCP occurred on or near the designated school routes. Therefore, it would make sense to implement traffic safety management and measures focusing on school routes. It was also found that the locational ESSVA risk structure is different depending on whether the purpose of the children’s travels is SCP or NSCP in the statistical model. Finally, it was suggested that evaluation of locational ESSVA risks based on the EB estimation is useful for efficiently extracting locations where traffic safety measures should be implemented compared to that only based on the number of accidents in the past.

This paper studies the relationship between activity pattern complexity and car use using two multi-day surveys involving the same participants but collected just before and about one year after they relocated their workplace. Measurable characteristics related to two latent variables, namely activity pattern complexity, or trip chaining (e.g., number of activities done within and outside the home–work tour), and to car use (e.g., usage rate, distance travelled by car) were selected. The study shows that the methodology adopted, partial least square structural equation modelling, quantifies the relation between the two variables, and is robust towards changes in important contextual characteristics of the individuals, namely workplace location. The findings indicate that the number of activities chained to commuting travels strongly impact mode choice and, in particular, car use. The paper also shows that chaining non-work-related activities has a stronger impact on car use. The results of this study suggest that planning and management solutions aimed at reducing car use, but focusing only on the commuting trip while neglecting the impact of other daily activities, may be less effective than expected.

The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents’ perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.

Many of our routines and activities are linked to our ability to move; be it commuting to work, shopping for groceries, or meeting friends. Yet, factors that limit the individuals’ ability to fully realise their mobility needs will ultimately affect the opportunities they can have access to (e.g. cultural activities, professional interactions). One important aspect frequently overlooked in human mobility studies is how gender-centred issues can amplify other sources of mobility disadvantages (e.g. socioeconomic inequalities), unevenly affecting the pool of opportunities men and women have access to. In this work, we leverage on a combination of computational, statistical, and information-theoretical approaches to investigate the existence of systematic discrepancies in the mobility diversity (i.e. the diversity of travel destinations) of (1) men and women from different socioeconomic backgrounds, and (2) work and non-work travels. Our analysis is based on datasets containing multiple instances of large-scale, official, travel surveys carried out in three major metropolitan areas in South America: Medellín and Bogotá in Colombia, and São Paulo in Brazil. Our results indicate the presence of general discrepancies in the urban mobility diversities related to the gender and socioeconomic characteristics of the individuals. Lastly, this paper sheds new light on the possible origins of gender-level human mobility inequalities, contributing to the general understanding of disaggregated patterns in human mobility.

With the rapid development of electric vehicles (EVs) around the world, debates have arisen with regard to their impacts on people’s lifestyles and urban space. Mining spatio-temporal patterns from increasingly smart city sensors and personal mobile devices have become an important approach in understanding the interaction between human activity and urban space. In this study, we used location-based service data to identify EV owners and capture the distribution of home and charging stations. The research goal was to investigate that how the urban form in regions under rapid urbanization is driven by EV use, from a geographical perspective. Using a case study of the expanding metropolis of Beijing, GIS-based spatial statistical analysis was conducted to characterize the spatial-pattern of the homes of EV owners as well as their charging preferences. Our results indicate that the spatial clustering of the homes of EV owners in non-urban central areas—suburban areas—is significantly higher than that in urban central areas. According to the records of visits to charging stations, the spatial interaction distance between the dwellings of EV owners and their visits to charging stations exhibits significant distance attenuation characteristics. 88% of EV owners in this research travels within 40 km (Euclidean distance) between housing and charging stations. At the same time, there were significant differences in the spatial patterns between working days and non-working days which are affected by commuting activities. The three types of urban spatial interaction patterns were identified and categorized by visualization. This transformation to EV use in the city influences several aspects of people’s decisions and behaviors in life. Understanding the impacts will provide valuable information for the development of EVs and their implications in the electrification of transportation, smart planning, and sustainable urbanization.

<p><strong>Abstract.</strong> The development of the sharing economy has provided an important realization path for urban’s green and healthy development, and has also accelerated the speed of urban development. With the constant capital pouring into the public transport field, dock-less shared bicycle is a relatively new form of transport in urban areas, and it provides a bikesharing service to fulfil urban short trips. Dock-less shared bicycle, with a characteristic of riding and stopping anywhere, has successfully solved the last mile travel problem. Recently, studies focus on the on the temporal spatial characteristics of public bicycle based on public bicycle operation data. However, there are few studies on the identification of riding patterns based on the characteristics of temporal and spatial behavior of residents. In addition, researches have been conducted on public bicycles administered by the government, and the dock-less shared bicycle have different characteristics from public bicycles in terms of scale of use and mode of use. This paper aims to analyze the temporal and spatial characteristics of residents using shared bicycles, and attempts to explore the characteristics of the riding modes of the dock-less shared bicycles.</p><p>Mobike sharing bicycle dataset of Beijing city were obtained for the research and this dataset contains a wealth of attributes with cover of 396600 shared bicycle users and 485500 riding records from May 10 to May 25 in 2017. Additionally, 19 types of POI (Point of Interest) data were also obtained through the API of Baidu Maps. To examine the patterns of shared bicycle trips, these POI data are categorized into five types including residential, commercial, institution, recreation and transport. Spatiotemporal analysis method, correlation analysis methods and kernel density methods were used to analyse the temporal and spatial characteristics of shared bicycle trips, revealing the time curve and spatial hotspot distribution area of shared bikes. Furthermore, a new matrix of riding pattern based on POI was proposed to identify the riding patterns during massive sharing bicycle dataset.</p><p>This paper aims to explore the riding behaviour of shared bicycles, and the research results are as follows:</p><p>(1) Temporal characteristics of riding behaviour</p><p>The use of the Mobike bicycles is significantly different on weekdays and weekends (Figure1). Figure 2 clearly shows a morning peak (7&amp;ndash;9&amp;thinsp;h) and evening peak (17&amp;ndash;19&amp;thinsp;h), corresponding with typical commute time. At noon, some users' dining activities triggered a certain close-distance riding behavior, which formed a noon peak. Different from the riding characteristics of the working days, there are many recreational and leisure riding behaviors on the weekends. The distribution of riding time is more balanced, and there is no obvious morning and evening peak phenomenon.</p><p>(2) Spatial characteristics of riding behavior</p><p> The spatial distribution of riding behaviour varies with different roads (Figure 2) and people prefer to choose trunk roads for cycling trips. Spatial hotpot detecting method based on the kernel density is applied to identify the active degree of bike sharing trip during a whole weekday (Figure 3). The red colour represents a high active degree and the green and blue colour means the low degree. Note that almost no riding occurred in the early hours of the morning and late at night. The characteristics of three riding peaks are obvious in the figure. A large number of travels occurred in Second Ring to Fourth Ring Road, and some travel activities were concentrated near traffic sites.</p><p>(3) Patterns of riding behavior</p><p> Different riding patterns happens in different space and change over the time at two scales of day and hour. During morning peak and evening peak on weekdays, more than 60 percent of riding trips are corresponding with typical commuting activities. The observed commuting pattern of morning peak (Figure 4(a) and (b)) implies that the majority of shared bicycle trips might relate to home, transports, commercial area and some institution. For example, students choose shared bicycles to do some school activities, people prefer to use shared bicycles as a connection tool to bus station and metro stops and people handle daily affairs in some government agencies. However, a large part of the shared bicycle trips on weekends shows the characteristics of non-commuting riding pattern, which means more leisure activities take place at weekends (Figure 4(c) and (d)). Non-commuting pattern of riding behavior mainly occurs among residential areas, metro stops, bus stations and recreational facilities, such as parks, playgrounds, etc.</p>

Understanding activity-travel patterns and their determinants with regard to multi-purpose commuters is essential for enhancing commuting efficiency and ensuring equal participation in activities. This study applies sequence analysis and hierarchical clustering to identify distinct activity-travel patterns of Kunming commuters using 2016 Household Travel Survey data. Subsequently, a multinomial logistic regression model (MNL) examines the factors influencing these patterns. The results reveal significant heterogeneity across four activity-travel patterns: the fixed commuter pattern (FCP), characterized by pronounced morning and evening peaks with minimal non-commuting activities; the balanced commuter pattern (BCP), where commuters participate in non-commuting activities after afternoon work; the restricted commuter pattern (RCP), with non-commuting activities occurring after midday work; and the flexible commuter pattern (FLCP), featuring a late-start work pattern where some commuters go to work after 5 pm. Additionally, the study finds that female commuters and those with longer commuting and working hours tend to have simpler time allocation. Conversely, male commuters, those from complex family structures, car-owning households, and residents in areas with abundant activity opportunities actively engage in non-commuting activities. These findings can help policymakers optimize travel services and develop heterogeneous commuting and transportation policies.

Understanding the specific effects of multidimensional elements of a built environment, transportation management policies, and the socio-demographics of travelers associated with commuting carbon emissions is significant for planners in promoting low-carbon and healthy urban development through transportation and land use and urban management policies. Most of the existing studies focus on the complex mechanisms affecting commuting behavior, but the relevant elements and specific mechanisms affecting commuting carbon emissions have not received sufficient attention. This study uses a random forest approach to analyze residential travel data from Wuhan, China. The results show that built environment and transportation demand management policies are critical to commuting carbon emissions, and that there is a non-linear association between multidimensional factors and commuting carbon emissions in Chinese cities. In addition, the study examines the synergistic effects of built environment and transportation management policies on commuting carbon emissions among different built environment elements. The results of the study provide valuable insights for planners in formulating low-carbon city and transportation development policies.

As a sustainable mode of travel, walking for transportation has multiple environmental, social, and health-related benefits. In existing studies, however, such walking has rarely been differentiated between commuting and non-commuting trips. Using multilevel zero-inflated negative binomial regression and multilevel Tobit regression models, this study empirically examines the frequency and duration of commuting and non-commuting walking and their correlates in Xiamen, China. It finds that (1) non-commuting walking, on average, has a higher frequency and longer duration than commuting walking; (2) most socio-demographic variables are significant predictors, and age, occupation, and family size have opposite-direction effects on commuting and non-commuting walking; and (3) different sets of built environment variables are correlated with commuting and non-commuting walking, and the built environment collectively influences the latter more significantly than the former. The findings provide useful references for customized interventions concerning promoting commuting and non-commuting walking.

De nos jours, les déplacements dans une agglomération sont souvent abordés selon les phénomènes de pointe avec des heures éponymes calquées sur les déplacements pendulaires (MUNCH, 2017). Or, sur certains territoires, les « autres motifs » deviennent prépondérants. C’est le cas des Bouches-du-Rhône, dont la Métropole d'Aix-Marseille Provence s’y confond quasi-intégralement (EMD BDR 2009). Cette dernière intéresse de par sa taille (1,9 millions d’habitants), sa jeunesse institutionnelle (créée le 1er janvier 2016) et son caractère multipolaire unique englobant le grand périurbain. Le but de ce travail croisant aménagement, mobilité, ingénierie et sociologie a été d’évaluer puis optimiser la gestion des flux de déplacements non pendulaires (DNP) engendrés par les pôles générateurs métropolitains afférents, en vue de l'adaptation des réseaux de transport public soumis à des objectifs de report modal.Nous avons tâché d’y répondre en deux parties. Il y a d’abord une première partie de cadrage avec la définition des pôle générateurs de déplacements non-pendulaires (PG DNP) puis du contexte géographique et institutionnel de notre terrain d’études en montrant son caractère hétérogène et encore mouvant avant de s’intéresser aux enquêtes de déplacements et leur conception vis-à-vis des DNP, offrant une connaissance macroscopique des déplacements locaux. Puis, la deuxième partie voit la création d’un protocole d’étude des DNP à travers l’idée que les PG DNP exercent une pression résultante de la fréquentation d’usage ainsi que de son environnement dont une meilleure connaissance peut permettre une réduction de cette pression d’usage. Une approche catégorielle des DNP a ainsi été appliqué et a abouti à cinq types de PG DNP que nous avons tâché de caractériser plus finement à travers l’étude de nouvelles données (billettique, circulation et GPS), ce qui a eu pour résultat de révéler les différents rythmes de fonctionnement des catégories de PG DNP. Enfin le dernier chapitre traitant des flux de DNP à l’aune des visions opérationnelles et citoyennes a révélé les difficultés d’adaptation aux DNP d’un outil de simulation tel que Metropolis 13+ si ces flux ne sont pas pensés nativement, ainsi que la prise en compte des DNP de manière indifférenciée vis-à-vis des autres types de déplacement, dans un contexte d’offre locale de transports en commun encore considéré insuffisante
Nowadays, commuting in an urban area is often addressed according to peak phenomena with eponymous hours modelled on commuting (MUNCH, 2017). In some jurisdictions, the “other grounds” become predominant. This is the case of the Bouches-du-Rhône, where the Aix-Marseille Provence Metropolis is almost entirely merged (EMD BDR 2009). The latter is interesting because of its size (1.9 million inhabitants), its institutional youth (created on January 1, 2016) and its unique multipolar character encompassing the large peri-urban. The aim of this work, which combines planning, mobility, engineering and sociology, was to evaluate and then optimize the management of non-commuting travel flows (NCT) generated by the related metropolitan generating poles, for the adaptation of public transport networks subject to modal shift objectives. We tried to answer it in two parts. There is first a first part of framing with the definition of non-commuting movement generating poles (NCT GP) the geographical and institutional context of our field of study by showing its heterogeneous character and still moving before being interested in travel surveys and their conception vis-à-vis the NCT, offering a macroscopic knowledge of local travel. The second part sees the creation of a protocol for studying NCT through the idea that GP NCT exert a pressure resulting from the use of frequentation and its environment, which a better knowledge can allow a reduction in this pressure. A categorical approach of the NCT was applied and resulted in five types of GP NCT that we tried to characterize more finely through the study of new data (ticketing, traffic and GPS), which resulted in revealing the different operating rhythms of GP NCT grades. Finally, the last chapter dealing with NCT flows in terms of operational and citizen visions revealed the difficulties of adapting to the NCT a simulation tool such as Metropolis 13+ if these flows are not thought natively, as well as the consideration of the NCT in an undifferentiated way vis-à-vis other types of travel, in a context of local public transport offer still considered insufficient

AbstractResidents’ over-reliance on motor vehicles has changed their travel modes, leading to many problems such as traffic and air pollution, and greatly affecting residents’ low-carbon, healthy living. Reducing carbon emissions from residents’ travel has become a social consensus. This paper studies the impact mechanism of residential patterns on residents’ travel based on carbon emission measurement. An empirical study was carried out in eight neighborhoods in Mianyang City, first statistically analyzing residents’ non-commuting trips, then calculating non-commuting carbon emissions by using the travel carbon emission measurement method, and then using multiple regression models to study the effects of each physical environment element in the settlement model on non-commuting carbon emissions. The aim is to explore the differences in the impact of different indicators, and the ultimate goal is to find out the factors that reduce residents’ travel carbon emissions and are conducive to the construction of healthy residential areas.The results show that there are differences in non-commuting travel and carbon emissions for different purposes in different residential areas, non-commuting travel is mainly by walking; carbon emissions from using service facilities are greater than carbon emissions from shopping and leisure travel. The impact of various elements of residential patterns is different, and the indicator elements with a greater degree of influence are floor area ratio, land use mix, and road network density.

AbstractResidential areas, as the main places for urban population activities, are the basic spatial units for reducing carbon emissions. The mix of residential land use functions is closely related to residents’ daily travel. This paper takes the Gaoshui district of Mianyang city as an example, uses carbon emissions as a measurement tool, constructs a family travel carbon emission quantitative estimation model and a land use function mix calculation model, compares and analyzes the differences in residents’ travel behavior in each sample area, and uses a linear regression model to analyze the impact of function mix on residents’ travel behavior. The results show that there are spatial differences in residents’ shopping, entertainment, medical treatment, commuting and other travel behaviors in the 12 sample areas; the function mix has no significant impact on family commuting carbon emissions, but has a significant negative impact on non-commuting carbon emissions, and mixed development of residential areas has a positive significance for reducing carbon emissions.

Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.

Google partnered with Via to launch an on-demand microtransit called Via2G between January and March 2020. The pilot provided employees with free travel to/from two of its offices in suburban, congested Silicon Valley. While the pilot was cut short due to COVID-19, rider participation grew steadily during operation. Of trip requests, 8,636 (87.8%) resulted in a ride offer. Unfulfilled requests were primarily outside of pilot operating times or when rider demand exceeded driver supply. Most users (72%) completed at least two trips, although recurring users were less likely to complete errands on the commute and fewer had a car available for commuting compared to all surveyed Google employees. Prior to Via2G, two-thirds (66%) of survey respondents drove to work at least one day per week, while a plurality (42%) drove five days per week. Compared to non-participants, pilot users were more likely to take ride-hail (14 vs 22 percent) or the Google Bus (24 vs 30 percent) at least once a week prior to the pilot. Recommendations suggest iterations for Google or other centralized employers to consider in future microtransit programs.