Trip Patterns Research Articles

Investigating the Correlation between Activity Similarity and Trip Similarity of Public Transit Passengers Using Smart Card Data

The pattern of activities by transit passengers and the factors affecting this pattern are recently addressed in the literature. Also, the pattern of trips and the factors affecting trips are the subject of many studies. However, little is known about the correlation between the activity and trip of passengers in the public transit network. This study investigates how similar are activities of passengers if they have similar trips (or vice versa)? And what factors impact the similarity of the activity and trip of passengers? Answering these questions is useful in understanding the mobilization patterns of passengers and developing group-based transit services. Also, smart card data have provided an opportunity, which was not available before, to analyze the activity and trip of the passengers in a large scale network. In this paper, the correlation between activity similarity and trip similarity of public transit passengers is investigated. The correlation between the activity and trip of the passengers is analyzed using histograms, Pearson correlation coefficient, conditional probabilities, and hexagonal binning technique. In addition, the impact of trip length and duration on the activity and trip similarity are examined using histograms and hexagonal binning diagrams. The proposed methodology is implemented for two-day smart card data in Brisbane, Australia. Results show that it is more likely to have the activity similarity when there is a trip similarity than having the trip similarity when there is activity similarity. Also, there is a nonlinear correlation between the activity and trip similarity with the trip length and duration.

Floating Car Data mining: Identifying vehicle types on the basis of daily usage patterns

This paper presents a novel approach for exploring vehicle daily mobility patterns using Floating Car Data in the Paris region. The objective is to reveal vehicle types and analyze usage patterns on different roadway sub-classes. Firstly, mobility representative features are recovered to recognize vehicle activity contexts, which includes modeling single trip pattern in terms of time-window, traveling-distance, and speed; and building vehicle mobility profile of trip combinations. Based on that, a two-step clustering algorithm is developed to explore the constitution of trip patterns and cluster vehicle types. Characterization analysis is conducted to find out outstanding features of clustered groups, thus helping to categorize the vehicles behavioral types. As a result, 4 major vehicle types were identified over the Paris region with the 2 comparative leading groups as those mainly composed by morning-activity trips and long-distance trips. Then, statistical association assessment by Configural Frequency Analysis is employed to examine the usage intensity on different roadway classes of identified types. The association analysis reveals that the identified types have statistically significant differences in the usage of different roadway classes. Furthermore, this approach can be expected to provide more representative results with more generalized sampling by the development of connected vehicles.

Weekday trip patterns of students in Obafemi Awolowo University, Ile-Ife, Nigeria

Weekday trip patterns of students in Obafemi Awolowo University, Ile-Ife, Nigeria

Karakteristik Wisatawan Nusantara yang Berkunjung ke Kota Solo

Solo City located in Central Java Province with potential cultural tourism. Tourist objects in Solo City were varied from traditional, namely Kasunanan Solo Palace, to modern, such as Beteng Trade Center. Tourists are freely to visit those tourist objects and bought souvenirs that were easily to found around the city. This study aim to describe tourist’s characteristics who visited Solo City. The characteristics were divided into demographic aspect, physiologic aspect, and tourist trip pattern. Method used in this study was mixed and questionnaires were distributed among 100 respondents, then result was describe into preferred aspects. The results were clustered into specific aspects. Demographic aspect showed that most of the tourist was woman, aged between 15-24 years old, the majority of respondents were not civil servant or industrial labor, high school graduated, and not married. Physiologic aspect showed majority of respondents visited Solo City no more than 12 hours, and spend mostly on trip cost. Lastly, from tourist trip pattern, majority were traveled with non-family group, for holiday purposed, and arranged independently. Key words: characteristic, demographic, physiologic, tourism, trip

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.

Non-Motorized Trip Pattern in Sub-Saharan Africa: Assessment of Walk Trip

Background:Walking as a parent mode of transportation has received little or no attention regarding planning, design, and development, thereby leading to chaotic outcomes when it comes to controlling and future estimation in most sub-Saharan Africa countries. This necessitated the research on the pertinent factors and variables that affect walking as a means of transportation in the study area, a semi-urban industrial cluster in South-Western Nigeria.Methods:One Thousand five hundred (1500) information cards were distributed for seven months. This was done to accumulate information on trip patterns, modal split, walk trip volume and so on. The data obtained was analysed using SPSS version 23.Results:The result revealed that 53.2% of the respondent engages in a walk trip every day either to access mode, access sub-mode or for recreation purposes. The commercial zone showed the highest percentage of walk trips of 40%. The mathematical model showed that age and walk trips are inversely proportional. Based on the result of the analysis, cost, safety and exercise are the primary reason why people engage in a walking trip. The R2value of 0.83 established the robustness of the model.Conclusion:The created model can be utilized for anticipating the future modular part by inputting anticipated future estimation of exploratory variables, which will also aid the planning of a better transport system for the locality. The lack of essential infrastructure poses a big problem for the utilisation of this mode of transportation.

Mobility dynamics of the elderly: Review of literatures

Mobility is key to maintaining independence, especially as people age. Sustainable development goals focused on providing access to safe, affordable, accessible and sustainable transportation for all by reducing number of global deaths and injuries from road traffic accidents. Providing transportation facilities for the elderly is an important part of sustainable transportation. This review assessed the mobility dynamics of the elderly with the aim of proffering sustainable solutions to the effective mobility of this unique set of individuals. Several literatures on mobility dynamics, travel behaviour and trip pattern of this unique set of people were assessed. The study showed that the use of public transport is influenced by distance in comparison with different modes of transport for the elderly. The review also established that distance is a function of the pendulum pattern. The mobility patterns of older people are influenced by various elements that depend on individual circumstances and state of health. Other factors that influenced mobility dynamics of the seniors as reviewed are age, gender, educational level, type of disability, income, residential area, availability of means of transport and built environment. The outcome of this review will aid transportation policy makers on the way-forward for effective and efficient mobility for the elderly now and in the foreseeable future towards a sustainable transportation system.

A Prioritization Method Considering Trip Patterns to Introduce Short-turn Buses

A Prioritization Method Considering Trip Patterns to Introduce Short-turn Buses

Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing

The rapid development of urban metropolises has attracted a growing number of immigrants and travelers, increasing the burden on transportation systems. Previous research on urban mobility patterns have ignored the temporal variations and heterogeneity in divergent urban trip makers due to the limited data resolution and coverage. In this paper, we analyzed cellular phone data of more than five million travelers for one month in Nanjing, China and proposed a method to extract trip origin and destination information from cellular phone signal data. We found that mobility patterns are different for urban residents, short-term travelers, and transfer travelers, and that trip length distributions can best be described by gamma and exponential distributions. In addition to the daily trip length distribution models, we utilized the agglomerative hieratical clustering method in order to group similar hourly trip patterns and further proposed within-day trip length distribution models under different times of the day and days of the week.

Understanding angler profiles in cases of heterogeneous count data – A travel cost model

Fishery managers need to understand how anglers will react to changes in the various social and ecological dimensions of the angling experience to develop optimal policies and management strategies. This knowledge requires a solid understanding of the anglers’ underlying preferences for angling attributes and how these preferences translate into angling trips. We use a travel cost method employing a stepwise estimation procedure to investigate how anglers’ preferences for site attributes affect the length of a trip, and we estimate welfare measures associated with recreational angling in the Tornionjoki River in Finland. We identify three distinct types of anglers, who differ substantially in their underlying preferences and angling trip patterns. For short visits (<3 days), anglers with a stronger selective angler profile tend to visit more often than other anglers. For longer visits, anglers with a stronger nature lover profile visit more frequently than others. Furthermore, anglers who catch more fish tend to make shorter visits and visit less frequently than others. These findings may help decision-makers to identify optimal fishery policies for cases such as the Tornionjoki River.

Understanding why: Push-factors that drive rural tourism amongst senior travellers

This research examined the push-motives driving domestic rural tourism amongst senior travellers. An understanding of this is essential to stimulate rural tourism and to redistribute visitors from high volume destinations to other locations. Applying the push-pull framework, an online survey of Australians aged over 50 was conducted. Participants were asked where they went and what motivated their trip. Based on the trip pattern followed, two rural trip segments were identified - those who preferred the east-coast (Eastern Staters), and those who did not have a geographic preference but travelled to remote destinations (Aussie Roundabouts). Rural travel was found to be driven by three motives, however, their influence differed based on the trip pattern. Eastern Staters were pushed to relax and escape, and experience novelty and adventure. Aussie Roundabouts travelled for a romantic getaway.

Secondary City and TOD Concept in the Light Rail Transit (LRT) Development to the Sustainable Transportation

Secondary city and several apartment developments in big cities in Indonesia both in secondary city and central city are the government’s effort in anticipating the society explosion in big city and the residence’s space need. But, with the ease access to the big city, it makes the satellite city to be more developed; inhabitant prefers to live in satellite city then works in big city. Those conditions will change the trip pattern and uprising the society movement from satellite city to central city. Suitable to what have said that in the developing country, life style will affecting the mobility, so with the ease of access and lot of private vehicle usage will add the road burden, where the impacts are traffic jam and lot of pollution in independent city and central city surrounding it. This study aims 1) to identify the characteristics and trip pattern of the study area, 2) to analyze the implementation of TOD concept in LRT development in the cities which have satellite city in Indonesia where the case study in Semarang city. Method in this study was performed using descriptive explorative and quantitative by simulating demand potential using TOD concept and after the LRT development. This study concludes that in the cities which have satellite city, TOD concept in LRT development will make a sustainable transportation and by using TOD concept in LRT development will change society life style.

Measuring individuals' travel behaviour by use of a GPS-based smartphone application in Dar es Salaam, Tanzania

A range of mega-cities in the Global South have started to invest in Bus Rapid Transit (BRT) systems, as a complement or replacement for informal paratransit services, in an effort to improve the mobility and accessibility in the city. Yet, few studies have tried to analyse the impact of such systems on the mobility patterns of cities' residents, in part because traditional travel diary surveys are often too expensive to conduct and unsuitable to capture spatial mobility patterns in fast growing cities with a high level of informality in spatial development. In this study, we analyse the applicability of a new method of data collection, i.e. a GPS-based smartphone application, to capture individuals travel behaviour in fast growing mega-cities in the Global South. Our case study is the city of Dar es Salaam (DES) in Tanzania, where the first BRT line is currently being implemented. In our study, the GPS-based app was used by individuals in DES to record distances, departure times and destinations of their trips. Socio-demographic data of respondents were recorded in short questionnaires. The spatial distribution of the trip patterns shows the mobility demand in both high and less connected areas. The results reveal a variation in departure times, travel destinations and trip distances that are one the one hand spatially limited within neighbourhoods and away from the planned BRT, and on the other hand along major roads connecting to the Central Business District (CBD). The short average distances of the trips (<3 km) reveal the characteristics of paratransit modes. The GPS-based smartphone application provides an opportunity to policy makers to engage deeply with the spatial reality of local communities, as a basis for transport investments and policy improvements as steps towards an integrated public transport system.

Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 20142016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.

Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning

In many big cities, the bike-sharing system (BSS) and taxi play critical roles in transportation services. They both offer on-demand transportation options and allow flexible riding scheduling and routing. Previous literature has compared BSS and taxi to other transport modes, such as public transit and private automobile, but little is known about the spatiotemporal factors that influence travel choices between these two alternatives. Understanding travel patterns of BSS and taxi is critical in traffic demand analysis and sustainable transportation planning. Also, an in-depth examination of the patterns of travel behaviors, especially when one would choose BSS over a taxi, will provide valuable insights on human mobility and active living research. In this study, we investigated the spatiotemporal patterns of BSS and taxi trips in Chicago from 2014 to 2016. To model travel choices between BSS and taxi, we applied machine learning techniques to simulate the means of transport based on environmental and temporal factors. Results show seasonal trip variations of the BSS and a declining trend of taxi trips. BSS speed is relatively stable while taxi speed varies primarily because of time and locations. Based on the random forest model, which has demonstrated the best fit with high processing speed, travel distance and the number of parks and recreational facilities seem to be critical spatial predicting factors of the travel choice. Given any time and location, the model can recommend the travel choices between BSS and taxis for users. This study shows the significance of machine learning techniques in urban mobility research. Results of the study may potentially support people's transportation decision-making and facilitate sustainable transportation planning.

An alternative to slow transit, drunk driving, and walking in bad weather: An exploratory study of ridesourcing mode choice and demand

Companies providing ridesourcing, or the use of mobile phone apps to request rides from drivers of privately-owned vehicles, have expanded rapidly in many cities in recent years. To shed light on this phenomenon, this paper reports an exploratory study of ridesourcing trip patterns and mode choice in Washtenaw County, Michigan, USA, which obtained a convenience sample of 167 respondents (reporting 192 trips) via geographically targeted online and offline ads. Consistent with previous empirical studies, ridesharing users are younger and a greater percentage are female than the the general public, and most trips occur in a small number of high density block groups. When asked what other options were available for ridesourcing trips, respondents reported transit (63%), private vehicles (32%), walking (32%) and bicycling (18%). Specific reasons for choosing ridesourcing instead of these options included the frequency of transit, alcohol use for driving, and weather and distance for walking and biking. A multivariate analysis found variables related to greater ridesourcing use for a block group included job density, jobs-housing balance, bar and restaurant density, and presence of households without vehicles. The paper demonstrates the potential of survey data to generate greater geographic insights into ridesourcing use, as well as the potential for extending established travel-behavior research approaches to ridesourcing.

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

A bikeshare station area typology to forecast the station-level ridership of system expansion

The continuous introduction and expansion of docked bikeshare systems with publicly available origin-destination data have opened exciting avenues for bikeshare research. In response, a flux of recent studies has examined the sociodemographic determinants and safety or natural environment deterrents of system ridership. An increasing abundance of disaggregate spatial data has also spurred recent calls for research aimed at extending the utility of these contextual data to model bikeshare demand and trip patterns. As planners and operators seek to expand bikeshare services into underserved areas, a need exists to provide a data-driven understanding of the spatial dynamics of bikeshare use. This study of the Washington, DC, metro region’s Capital Bikeshare (CaBi) program answers this call by performing a latent class cluster analysis to identify five bikeshare station area types based on variation in a set of land development pattern, urban design, and transportation infrastructure features. This typology is integrated into a planning application exploring the potential for system expansion into nearby jurisdictions and forecasting the associated trip-making potential between existing and proposed station locations.

Empirical evaluation of influential factors on bifurcation in macroscopic fundamental diagrams

Observations from empirical data in the roadway network showed that the relation between averages of network flow versus density often exhibit hysteresis and bifurcation phenomena, which may obscure the reproducibility of a well-defined macroscopic fundamental diagram (MFD). In this paper, we analyzed large-scale trip data from passenger vehicles in an urban network of South Korea and evaluated the shapes of MFDs over many days. It was found that MFDs have two distinctive, reproducible forms: a well-defined, unique relation on weekends and a bifurcation in high-density regime on weekdays.With regard to the bifurcation, we observed higher network flows in the morning and lower network flows in the evening for the same average network density. This implies that the same set of drivers in the network collectively formed two different trip patterns. Hence, we evaluated possible factors that may have effects on the bifurcation phenomenon. In view of this, four factors – heterogeneity, trip completion rate, detouring ratio and commute trips – were analyzed in this study. The findings showed that travelers’ detours could be a key factor for the occurrence of bifurcation because, by detouring travelers improve neither their own travel times nor network-wide travel times, and thereby degrade the network production.

Analyzing Spatio-Temporal Distribution Pattern and Correlation for Taxi and Metro Ridership in Shanghai

Taxicab is an important mode in urban transportation system, while the role of taxicabs, especially the relationship with metro system has not been fully studied. This study aims at exploring the factors influencing the role played by taxicabs in Shanghai, China. Firstly, taxi trips are categorized into three types, namely metroreplaceable (MR), metro-extending (ME) and metro-supplement (MS) ones. Then, the tendency of travelers towards taxi or metro at a specific metro station is proposed and calculated on the basis of MR taxi trips and metro trips. Factors influencing the tendency are investigated through semi-parametric regression models, with the results indicating that the most significant factors and the influencing radii during the peak and off-peak hours are different. Some built environment factors, such as the number of hospitals and government agencies, have significant positive relationship with the tendency in the time periods. Furthermore, land use related factors, such as the increase of forestry and commercial land, generally promote taxi-hiring in the off-peak hours, while they have a negative impact during the peak hours. Findings of this study can assist governments and policy makers to understand the impact of built environment and land use on trip patterns, and thus may contribute to more reasonable policies and optimized urban planning, which may promote modal switch from taxi to subway.