Trip Destination Research Articles

Placemaking as an urban development strategy for making the Pattaya Innovation District

AbstractPlacemaking is a fundamental concept regarding the development of innovation districts, aiming to achieve an attractive and efficient physical design that makes the districts pleasant places to live and work in. The concept also emphasizes the process of neighbourhood creation that meaningfully respects and shows an understanding of place identity as well as social interactions, which are the foundation of knowledge‐based communities focusing on creativity, innovation, and research development. This study adopted social network analysis (SNA) as a tool to examine the daily activities of the people who live and travel in Pattaya City, Thailand's popular coastal tourist destination. Trip destinations and frequency of traveling could reflect some critical nodes of the activities. Spatial nodes and ties could be further developed into innovation districts that attract innovators and researchers aiming to create innovations that respond to the needs of the people. The questionnaire examining daily socio‐economic activities was distributed to 1,396 respondents, comprising three major groups of stakeholders: tourists, local people, and entrepreneurs. The findings showed that they routinely visited department stores, governmental offices, banks, and hospitals, located between North Pattaya and Central Pattaya. Such an area could potentially be developed as an innovation district, which means providing not only a cluster of jobs that generates innovation to serve local and travelling needs, but also a cluster of housing that creates a work and life balance. The analysis emphasizes the link between physical design and regional‐scaled policies that are often overlooked in policy implementation to promote innovations.

Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

What affects how far individuals walk?

The number of studies that explore contributing factors that encourage individuals to do more walking trips is proliferated in recent years. However, there is still a lot to know about differentiating between short and long walking trips and their associated influencing factors. The current research investigated the impact of the influencing factors on the share of short and long walking trips across four different trips in 112 traffic analysis zones of Rasht, Iran. The share of walking trips was defined as the proportion of walking trips that originated/ended in an area on all trips that originated/ended in that area. In four trip purposes in short, long, and all trips, the factors associated with the share of walking in origins and destinations were investigated in 24 separate models. The factors included built environmental indices, such as transportation network connectivity and land-use variables, as well as socio-demographic. To differentiate between short and long walking trips, 600 m walking distance was recognized as a proxy. According to the results, the population density was found significantly increase share of walking in both the origins and destinations of short walking trips. Moreover, the models’ goodness of fits were relatively higher in short walking trips comparing long walking trips. This research's findings would give a profound assessment to city planners and decision makers who favor expanding walking as a sustainable mode of transportation.

Changes in the Pattern of Bikeshare Usage due to the COVID-19 Pandemic

Bikeshare use in New York City dropped substantially during the initial months of the COVID-19 pandemic, but by summer of 2020 had largely recovered to pre-pandemic levels. The patterns of usage, however, have changed. In this study we compare bikeshare trip patterns in September 2020 to September 2019, restricting our analysis to the Borough of Brooklyn in New York City. We focus on user types, time of day, and changes in the origin and destination of trips, based on aggregated land uses surrounding docking stations. Our results suggest a shift toward longer trips and more recreational trips with fewer work-related trips.

Bikeshare destination choices and accessibility among disadvantaged communities

There is a lack of understanding of trip destination choices and behaviors for bikeshare users from disadvantaged communities, which are usually underrepresented in bikeshare users’ profiles. Consequently, this study develops a competing destination model to analyze the spatial patterns of choice parameters of bikeshare trips. This research highlights the fact that areas with improved accessibility (i.e., the capability to access different mobility services and technologies, and/or to reach key destinations/opportunities) are more likely to attract bikeshare trips. Among user types (i.e., annual members, day-pass users), annual members from disadvantaged areas are more likely to travel longer distances to other areas that offer more services and opportunities. However, these users are more sensitive to the extra charge levied after the 30-minute grace trip time. This study provides practical suggestions towards designing equitable bikeshare systems to make the social benefits of bikeshare available to all.

SPATIOTEMPORAL DYNAMICS OF PUBLIC TRANSPORT DEMAND: A CASE STUDY OF RIGA

Sustainable urban mobility remains an emerging research topic during last decades. In recent years, the smart card data collection systems have become widespread and many studies have been focused on usage of anonymized data from these systems for better understanding of mobility patterns of Public Transport (PT) passengers. Data-driven mobility patterns can benefit transport planners at strategic, tactical, and operational levels. A particular point of interest is a spatiotemporal dynamics of mobility patterns that highlights transformation of the PT passenger flows over the time continuously or in response to modifications of the PT system and policies. This study is aimed to estimation and analysis of the spatiotemporal dynamics of PT passenger flows in Riga (Latvia). A multi-stage methodology was proposed and includes three main stages: (1) estimation of individual trip vectors, (2) clustering of trip vectors into spatiotemporal mobility patterns, and (3) further analysis of mobility patterns’ dynamics. The best practice methods are applied at every stage of the proposed methodology: the smart card validation flow is used for extracting information on boarding locations; the trip chain approach is used for estimation of individual trip destinations; vector-based clustering algorithms are utilised for identification of mobility patterns and discovering their dynamics. The resulting methodology provides an advanced tool for observing and managing of PT demand fluctuation on a daily basis. The methodology was applied for mining of a large smart card data set (124 million records) for year 2018. Most important empirical results include obtained daily mobility patterns in Riga, their clusters, and within-cluster dynamics over the year. Obtained daily mobility patterns allows estimation of a city-level PT origin–destination matrix that is useful in many applied areas, e.g., dynamic passenger flow assignment models. Mobility pattern-based clustering of days allows effective comparison and flexible tuning of the PT system for different days of a week, public holidays, extreme weather conditions, and large events. Dynamics of mobility patterns allows estimating the effect of implementing changes (e.g., fare increase or road maintenance) and demand forecasting for user-focused development of PT system.

A Hybrid Agent-Based Simulation and Optimization Approach for Statewide Truck Parking Capacity Expansion

Abstract Truck parking shortages are a critical concern for both the trucking industry and truck drivers in the USA. To tackle growing shortages, parking capacity expansions are needed. This paper presents a hybrid agent-based simulation and optimization approach to model truck movements and driver behaviors to determine feasible locations for truck parking facility capacity expansions across a state. The simulation model considers the driving limit and rest requirements of hours-of-service regulations set forth by the federal regulations. By leveraging observed historical truck GPS data, agent profiles are created, capturing truck trip origins, necessary stops, trip destinations, and parking behaviors. The simulation model estimates parking facility utilization over time by identifying where, when, and how long truck drivers rest. The estimated parking usage data is then fed into a maximal coverage capacitated multiple facility location optimization with multiple service model to deduce capacity expansion locations given budgetary restrictions. Ultimately the model recommends where and how many parking spaces to add to accomplish a certain level of parking overcrowding. The simulation and optimization approaches are finally integrated into a map-based, user adaptable decision support tool.

The impact of COVID-19 pandemic on travel behaviour of commuters in Makurdi metropolis

The outbreak of Corona Virus (COVID-19) in the 4th quarter of year 2019 at Wuhan city of China, and its rapid spread across the world necessitated the introduction of travel restriction policies by the federal and state governments of Nigeria. This was to help curtail the spread of the deadly disease. This study examined the travel behaviour of flouting commuters in Makurdi metropolis amidst the travel restriction policy during the lockdown period. About 496 questionnaires were administered to commuters travelling within the 16 km radius metropolis. Essential factors captured in the questionnaire included modal split and trip purposes as affected by socio-demographic characteristics of commuters such as; age, gender, marital status, level of education, etc. The findings of the study revealed that there was no statistically significant level of dependence of commuters’ socio-demographic characteristics on modal split and trip destinations at 5% level of significance using contingency tables. The trend revealed that there were no obvious influential changes in the travel behaviour of commuters in Makurdi metropolis amidst the COVID-19 pandemic. It was recommended that concerned authorities should create more awareness about the outbreak of pandemics to help instil fear in commuters and discourage travelling to prevent wide spread. Also, distribution of palliatives to households during lockdown periods to enable them cope with the situation as they remain indoors, and deployment of law enforcement agents to streets and highways to stop commuters from travelling during lockdown periods to minimise the spread of communicable diseases were recommended.

Practical model with strong interpretability and predictability: An explanatory model for individuals' destination prediction considering personal and crowd travel behavior

AbstractReal‐time individuals' destination prediction is of great significance for real‐time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this paper, taking the public transportation metro systems as example, we design a practical method called practical model with strong interpretability and predictability to predict each passenger's destination. Our main novelties are two aspects: (1) We propose to predict individuals' destination by combining personal and crowd behavior under certain context. (2) An explanatory model combining discrete choice model and neural network model is proposed to predict individuals' stochastic trip's destination, which can be applied to other transportation analysis scenarios about individuals' choice behavior such as travel mode choice or route choice. We validate our method based on extensive experiments, using smart card data collected by automatic fare collection system and weather data in Shenzhen, China. The experimental results demonstrate that our approach can achieve better performance than other baselines in terms of prediction accuracy.

A study on the effect of tourist attributes and travel patterns on sightseeing excursions

Kyoto City in Japan has 17 world heritage spots that are referred to as the Historic Monuments of Ancient Kyoto. It has long been chosen as a school trip destination for Japanese students and is recognised as an exemplary destination for domestic travel, and is considered as one of the top destinations in the world to visit. However, the visiting crowds tend to congregate in just a few places, which leads to overcrowding and can make tourist attractions less appealing. This can have a detrimental effect on a city's tourism and lead to a sharp fall in revenue. With that in mind, a team of researchers, represented by Dr Yoshiaki Kouchi, in the Department of Public Policy within the Faculty of Sociology, Bukkyo University in Japan is studying the effect of tourist attributes and travel patterns on sightseeing excursions.

Modal Mismatch and Accessibility Gap in Dhaka using a Time of Day Based Approach

Transport accessibility is an area of growing global attention among transportation planners and policymakers. This paper aims to portray spatiotemporal variations of car and public bus accessibility in the context of a city in a developing country: Dhaka, Bangladesh. The public bus system in Dhaka is characterized by a semi-formal arrangement which means there is a lack of available data, for example, General Transit Feed Specification, which poses great difficulties in the study of accessibility. Given these limitations, we have presented the concept of major destinations to analyze spatiotemporal accessibility based on the simple understanding that trip purposes, time of day, and trip destinations are interlinked in an urban area, and different locations would attract a different number of trips based on the time of day. Using a spatial autocorrelation approach, we identified the statistically significant destination clusters in Dhaka by peak and off-peak hours. We measured accessibility to the major destinations using a cumulative opportunity-based metric followed by estimation of the Modal Accessibility Gap (MAG). The findings indicated that, regardless of the trip origins and time of day, dependence on public transport puts the users in a substantially disadvantageous position. From the policy perspective, we have suggested the introduction of a formal public transport system in Dhaka, particularly targeting the traffic analysis zones with higher MAG. Such an approach would lead to better resource usage while providing enhanced public transport services for both peak and off-peak hours and limiting dependence on cars.

The incidence, the most common symptoms and risk factors of altitude sickness in children

High-altitude tourism is gaining popularity. Mountains are also becoming an increasingly popular destination for school or family trips. This suggests that the number of children with high-altitude diseases, including acute mountain sickness (AMS), will also rise. The aim of this literature review was to determine the epidemiology of acute mountain sickness, its most common manifestations and risk factors in children. We analysed papers from the last 5 years, which were found by two independent authors using PubMed, ResearchGate and Google Scholar. The following keywords were used to identify relevant studies: acute mountain sickness, altitude sickness, children (by Medical Subject Headings). After screening with the exclusion criteria, the final analysis included 5 papers, which were assessed for the quality of evidence. The incidence of acute mountain sickness in children is 30–45% at elevations below 4,000 m. There were no differences compared to an adult population. Headache, sleep disorders and dizziness are the most common symptoms of acute mountain sickness in children. Further research is needed to identify factors that predispose children to this disease. In the light of the increasingly intensive alpine tourism among children, research on the sequelae of acute mountain sickness is recommended.

Choice behavior of tourism destination and travel mode: A case study of local residents in Hangzhou, China

The social and economic growth as result of promoting the rapid development of tourism in China has brought tremendous pressure on the urban transportation systems. Research of travel behavior concerning the characteristics of tourists has provided effective information for transportation planning. Due to different city plans, public transportation system design, car parking design and management, etc., the local situation in developed countries differs from the counterpart in China. However, little research has studied the factors influencing the choice of travel destinations in tourism. The research aims to study the tourism destination and mode choice behavior of tourists and provides suggestions to improve tourism transportation service system. An online questionnaire survey is used to collect data including the travel characteristics and personal attributes of local tourists in different holidays in Hangzhou, China. A multinomial logit model is constructed with the trip destination set as the dependent variable. Results show that age, residential type, car ownership, companion type and holiday length have a significant impact on destination choice. To determine what influences modal choice for such trips, a second logit model is established with travel mode set as the dependent variable with the explanatory variables of age, gender, companion type, car ownership, holiday length and travel destination found to be significant. The results demonstrated that people aged 26 to 44 prefer suburban areas, and they are the main group driving to their travel destination. Public transport use frequency decreases when the destination is located outside of the main tourist area. Finally, suggestions have been proposed to mitigate the congestion and parking problem based on model analysis from the perspective of the bus line setting, transfer improvements, and the policy to limit cars, respectively.

Mining non-redundant distinguishing subsequence for trip destination forecasting

Trip destination forecasting has received a great attention recently when the idea of intelligent transportation is discussed ubiquitously from related business to government. For instance, we gain convenience such as easily finding a ride at the right location for the right time in a car dispatch system given the sharing economy setting. In this work, we aim to propose a method for trip destination forecasting given its first partial trip trajectory as the input. In a nutshell, we formulate the problem as a multi-class prediction problem and mine the distinguishing pattern that we can see on one class but not on other classes. Moreover, we attempt to find non-redundant rules to separate the interested class from other classes by an efficient algorithm called Non-redundant Contrast Sequence Miner given multiple answers (destination) to choose from. This study tested the proposed method on public trip destination prediction dataset. The results show that the proposed method outperforms other mining techniques on the task of trip destination forecasting in terms of accuracy and resource allocation both time and memory usage efficiency and accuracy.

Reaction to a Pandemic: Social Distancing and Driving Among Older Adults During COVID-19.

Coronavirus disease 2019 (COVID-19) has produced considerable morbidity and mortality worldwide, and older adults are at especially high risk for developing severe COVID-19. A cohort study of driving behavior from January 1, 2019, to April 25, 2019, and January 1, 2020, to April 25, 2020, was conducted. We hypothesized that older adults would reduce the number of days driving and number of trips/day they make after COVID-19 case acceleration. Data from 214 adults aged 66.5 to 92.8 years were used. Women comprised 47.6% of the sample and 15.4% were African American. Participants reduced the proportion of days driven during the pandemic (.673 vs. .382 [p < .001]) compared with same period the year before (.695 vs. .749). Trips/day showed a similar decline (p < .001). Participants also took shorter trips (p = .02), drove slower (p < .001), had fewer speeding incidents (p < .001), and had different trip destinations (p < .001). These results indicate that older adults reduce their driving behavior when faced with a pandemic.

On the importance of trip destination for modelling individual human mobility patterns.

Obtaining insights into human mobility patterns and being able to reproduce them accurately is of the utmost importance in a wide range of applications from public health, to transport and urban planning. Still the relationship between the effort individuals will invest in a trip and the importance of its purpose is not taken into account in individual mobility models that can be found in the recent literature. Here, we address this issue by introducing a model hypothesizing a relation between the importance of a trip and the distance travelled. In most practical cases, quantifying such importance is undoable. We overcome this difficulty by focusing on shopping trips (for which we have empirical data) and by taking the price of items as a proxy. Our model is able to reproduce the long-tailed distribution in travel distances empirically observed and to explain the scaling relationship between distance travelled and item value found in the data.

A reputation-based framework to support dynamic car-pooling1,2

In the last decades, individual urban traffic flows have increased all over the world with a consequent growth of road congestion and environmental pollution. In this context, car-pooling is an interesting car-based alternative to satisfy the individual mobility demand by optimizing the car loading factor with respect to the number of passengers, provided that all the participants share trip origin and destination in the same time slot. To make the system more appealing, this paper proposes an on-demand car-pooling service adopting variable fares, on the basis of trip length and number of participants. Multi-agent, reputation and recommender system technologies in synergy with a routing algorithm have been used to this aim. Experiments on simulated data proved the potentiality of the proposed approach.

Origin-Destination Flow Estimation from Link Count Data Only.

All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.

Spatial distribution of ride-hailing trip demand and its association with walkability and neighborhood characteristics

Despite the recent growth and popularity of ride-hailing services throughout the world, there's still a lack of research on its determinants. This paper aims to examine the associations between ride-hailing and their spatial distribution in relation to key socioeconomic and built environment characteristics both at the trip origin and destination. To do so the study uses official data provided by Transportation Network Companies operating in the city of Chicago, with 32 million trips logged between November 1st, 2018 to June 28th, 2019. Among the built environment attributes we focus on the relationship between walkability levels and demand for ride-hailing. Study findings indicate an association between ride-hailing and income levels, car-availability and race-ethnicity. Results also suggest a positive association between walkability at either trip origin or trip destination and ride-hailing demand, together with a negative one between access to transit and ride-hail use. Findings suggest some worrisome conclusions, with ride-hailing being seldom used among the more deprived areas. Ride-hailing is predominantly being used to travel between highly accessible areas which should be accessed using more sustainable transport modes. Positive takeaways are the lack of race disparities in ride-hailing demand and the capacity of ride-hailing to interact and complement public transit provision.

Identifikasi Perilaku Perjalanan Melalui Metode Critical Path Method (CPM)

Mobilization in urban areas has increased every year along with the population growth and urban's development. Travel behaviors of the population in urban areas can be classified based on their travel characteristics. The identification of communities' travel behavior of Yogyakarta is based on the origin - destination and length of trip based on age characteristics. It will effects the travel patterns in the urban environment through spatial mapping. The method used in this research is critical path method. Critical Path Method (CPM) is used to determine the optimum path taken by the community based on its duration, which in this study CPM is used to determine the average travel of the community based on age groups when viewed from distance, length of trip and the number of daily trips. Based on the analysis, it shows that the average age of 15-22 years and 31-37 years have the highest travel time, which is about 9 hours and the average age of 23-30 years traveled the longest by 12.5 km. The average mobilization of travel behaviors in KPY is to the urbancenter of Yogyakarta. The KPY urban form does not have a compact form.