TYPOLOGY OF PRIVATE CAR USERS DURING COVID-19 PANDEMIC IN KAYURINGIN JAYA

<p>The Covid-19 Pandemic was indicated in March 2020, which has changed people's daily activities patterns. Implementing the restricting regulation imposed by the government made some of the people's daily activities diverted to an online system. As a result, community mobility has decreased, especially on private car usage. However, there is a shift in vehicle usage which many people are starting to switch their mode to the private car in daily travel. This condition was predicted would continue even after the Pandemic ends. The increase in private car usage will worsen the congestion than before the Covid-19 Pandemic appropriate steps and handling are needed to prevent the increase in congestion. One of them is by knowing the characteristics and journeys of private car users during the Covid-19 Pandemic. This research is a typology of private car users during the Covid-19 Pandemic to identify the similarities and differences in the characteristics possessed by each private car user through the typological groups formed. Through this research, it can be seen the movement patterns and characteristics of the people who use private cars. This study uses the Hierarchical Cluster Analysis method. The analysis is based on several variables such as private car usage frequency variables, socioeconomic characteristics variables, demographic variables, household variables, and household travel patterns object of this research is 107 households which are owners and use of private cars for further analysis and form clusters of private car users that have the same characteristics of each cluster. The typology of private car users is compiled based on the unique characteristics possessed by each cluster that is formed. The results of this study are 8 typologies of private car users, which are divided from intensive users to irregular users. Typology 1 has the largest number of respondents and dominates the frequency of trips by private car users. The benefit of this research for the government is as input in the formulation of policies to regulate the use of private cars so that the policies taken by the government can be right on target</p>

Gender Differences in Environmental Correlates of Cycling Activity among Older Urban Adults

Cycling is a form of active transport that can improve the level of health among the elderly population. However, little is known about the environmental correlates of bicycle use among older adults. This study investigated the relationship between the built and social environment and the gender differences in cycling frequency among older urban adults in China. The data were derived from a household travel survey in 2012 and covered thirty-three urban neighborhoods in Zhongshan. The results suggest that denser intersections are negatively related to cycling trips among both older men and women. Reverse associations for either gender, however, are observed between the average income in a neighborhood and cycling frequency. For older women, living far from a bus stop is positively correlated to an increase in daily cycling trips. For older men, social environment, including the proportions of employed or elderly people in a neighborhood, is significantly associated with cycling activity. The findings facilitate the understanding of the gender gap in cycling activity among older urban adults, and help towards designing effective planning strategies as health interventions.

Joint Decisions in Residential Choice

<p>Economic agents frequently make joint decisions, which often require a compromise by some or all of the participants. We propose an econometric model in which groups of agents make a joint decision; each agent has preferences modelled using a combination of multi-nominal logit and conditional logit parts. We combine these marginal preferences to create a joint set of probabilities of the group making a particular choice, which enables parameter estimation by maximum likelihood. We can also make the weight applied to an individual agents preferences depend on characteristics of the agent or group. To demonstrate the use of the model, data is obtained from the New Zealand Household Travel Survey. We estimate our model to show how households might make the joint decision of where to live, given that different household members have different work locations.</p>

Joint Decisions in Residential Choice

<p>Economic agents frequently make joint decisions, which often require a compromise by some or all of the participants. We propose an econometric model in which groups of agents make a joint decision; each agent has preferences modelled using a combination of multi-nominal logit and conditional logit parts. We combine these marginal preferences to create a joint set of probabilities of the group making a particular choice, which enables parameter estimation by maximum likelihood. We can also make the weight applied to an individual agents preferences depend on characteristics of the agent or group. To demonstrate the use of the model, data is obtained from the New Zealand Household Travel Survey. We estimate our model to show how households might make the joint decision of where to live, given that different household members have different work locations.</p>

Association between network characteristics and bicycle ridership across a large metropolitan region

Background: Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership.Methods: We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated and Bayesian spatial models were used to explore the association between these network characteristics and bicycle ridership (measured as counts of the number of trips, and the proportion of all trips that were made by bike). Results: We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region.Conclusions: These findings challenge the utility of approaches based on spatially modelling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. There is a need to progress the science of measuring safe and connected bicycle networks for people of all ages and abilities.

What Counts as Commute Travel? Identification and Resolution of Key Issues around Measuring Complex Commutes in the National Household Travel Survey

We use travel diary data from the 2017 National Household Travel Survey (NHTS) Georgia subsample to address critical issues associated with analyzing complex work journeys. To define the work journey, we discuss the importance of defining commute anchors by both purpose and location. We then compare two alternate measures for determining what portion of each journey should be counted as commute distance: the last leg of the journey (the NHTS default), and a modeled counterfactual simple commute to estimate the distance that would have been traveled had no stops been made. The average complex commute distance obtained using the counterfactual method was 63% higher than the estimate based on using the last leg alone. Using the last-leg method may understate Georgia’s annual commute distance by 2.6 billion miles (10% of the total, including both simple and complex commutes). We argue that the last-leg method is not an accurate gauge of work travel, particularly among populations such as women, who are more likely to trip chain on their commutes.

Where are the low-hanging fruits? Strategic mapping of potential consumers of shared e-mobility to help decarbonise mobility of our cities.

In marketing research, the concept of ‘low-hanging fruits’ refers to those consumers who are easiest to attract to a business. Focusing efforts on this group maximizes the effectiveness of a marketing campaign. In mobility planning, this concept could be adopted by city planners more often to achieve sustainability goals.Imagine that a start-up just launched a new model of shared e-scooter in a busy town like Rotterdam. It is natural to expect that, for the sake of financial sustainability, a significant part of the revenue should come from neighbourhoods that cluster factors of success for potential usage (e.g. commercial activities, jobs, good infrastructure). However, if shared e-mobility is meant to cause significant and positive impact on sustainability, helping cities achieve their goals, further structural changes in travel habits are certainly necessary. In short, part of the ‘unnecessary’ car trips should be more often replaced by more sustainable modes, like shared e-mobility. ‘Unnecessary’ is interpreted in this study as a car trip that has a similar profile (e.g. length, travel time, socioeconomics) of a shared e-mobility trip, and therefore could be ‘avoided’ or ‘replaced’ by more sustainable alternatives. The individuals making those trips are called ‘low-hanging fruits’, but are ‘not yet consuming the product’. How to map low-hanging fruits? In this study, an approach is proposed to help providers and cities strategically map them. The approach is operationalised in the context of the Netherlands, a country where shared e-mobility is spreading quickly. The approach can be divided in 3 major phases: 1) Characterising a typical ‘avoidable’ car trip in the context of a given population (city, region, country), through the investigation of how current users of shared mobility travel (e.g. trip distance, duration) and their characteristics (e.g. age, gender, income); 2) Mapping where the avoidable car trips are generated, since countries like the Netherlands keep their Household Travel Surveys up to date so that city planners can use that information to extract insights of travel habits (desire lines, purpose, mode, etc); 3) Labelling locations in regard to their likelihood of having more or less low-hanging fruits, through the application of unsupervised learning (k-means) to find probable clusters of low-hanging fruits. In order to achieve (1), we used an anonymous, ‘privately acquired’ shared mobility OD travel matrix produced in 2020 by a third party mobility company. This OD refers to trips done by e-scooter users of Rotterdam during the summer of 2020. For (2), we explored the latest Dutch Household Travel Survey (2020) and combined it with (1). This type of survey provided annual information about daily travel patterns of more than 60.000 people. The Dutch HTS can also be expanded to mitigate negative impacts of data collection biases and be a reasonable representation of how the whole population travels on a daily basis. In (3), we combined insights extracted from (2) with Census information to perform the unsupervised classification of locations. We propose and operationalise a pragmatic approach to help cities and mobility providers identify potential users of shared mobility. If shared mobility could seduce more low-hanging fruits, significant environmental impacts from modal shift could be achieved. Some use cases of this exercise can be applied to:(i) size potential market for expansions (e.g. deployment of vehicles or installation of facilities); (ii) size potential impacts of modal shift on city-wide Co2 emissions; (iii) design subsidies that encourage providers to deploy assets in certain areas; (iv) change fees depending on the potential to attract former private vehicle users; (v) investigate reasons behind the existence of avoidable car trips.&nbsp;

The Impact of Geo-Spatial Distribution of Markets on Household Travel Patterns in Akure Metropolis, Nigeria

This study examines the spatial distribution of markets and its impact on household travel patterns in Akure, Nigeria. Both primary and secondary data were utilized for the research. The coordinates of locations of markets were obtained using a hand held Geographical Position System (GPS) and determined by measured using the ‘Ruler’ menu of ArcGIS 10.3.1 software. A structured questionnaire was designed to elicit information on household travel patterns. Using descriptive and inferential statistics, the findings reveal that market is randomly distributed. A linear association exits between distance travel to market and household mode choice of transportation and it is significant at 0.05%. Inadequate transport services and traffic congestion are problems faced by households in Akure on their trips to markets. The study concludes that more periodic or daily markets should be provided in some localities that do not have in Akure and similar other cities in Nigeria.

Glimpse of the Future: Simulating Life with Personally Owned Autonomous Vehicles and Their Implications on Travel Behaviors

To explore potential travel behavior shifts induced by personally owned, fully autonomous vehicles (AVs), we ran an experiment that provided personal chauffeurs to 43 households in the Sacramento region to simulate life with an AV. Like an advanced AV, the chauffeurs took over driving duties. Households were recruited from the 2018 Sacramento household travel survey sample. Sampling was stratified by weekly vehicle miles traveled (VMT), and households were selected to be diverse by demographics, modal preferences, mobility barriers, and residential location. Thirty-four households received 60 h of chauffeur service for 1 week, and nine households received 60 h per week for 2 weeks. Smartphone-based travel diaries were recorded for the chauffeur week(s), 1 week before, and 1 week after. During the chauffeur week, the overall systemwide VMT (summing across all sampled households) increased by 60%, over half of which came from “zero-occupancy vehicle” (ZOV) trips (when the chauffeur was the only occupant). The number of trips made in the system increased by 25%, with ZOV trips accounting for 85% of these additional trips. There was a shift away from transit, ridehailing, biking, and walking trips, which dropped by 70%, 55%, 38%, and 10%, respectively. Households with mobility barriers and those with less auto dependency had the greatest percent increase in VMT, whereas higher VMT households and families with children had the lowest. The results highlight how AVs can enhance mobility, but also caution against the potential detrimental effects on the transportation system and the need to regulate AVs and ZOVs.