Trip Destination Research Articles

Vehicle operating speeds in southwestern Colombia: An important database for the future implementation of optimization models for geometric design of roads in mountain topography.

The essential objective of a road is framed in allowing the circulation of vehicles from a point of origin to a destination, being essential to fulfil completely this function, consider aspects such as: functionality, safety, economy, comfort, environmental integration and harmony or aesthetics. For this effect, a geometric design based on consistency must be achieved, which arranges the elements of the road and their geometric characteristics, helping to minimize adverse effects on the driver's expectations, perceiving homogeneity on the route, without abrupt variation in the level of attention necessary and thus be able to adapt to the changing geometric conditions of the road. In order to assess the consistency in the geometric design, different criteria and methodologies strongly related to the level of accident rates have been developed, which in turn are based on the analysis of the evolutions of the operating speeds. By virtue of the above, speed is therefore of vital importance to carry out any type of evaluation or analysis of traffic, since it is an important indicator of the quality of the service offered to users, because of it is function of the physical characteristics of the road and its environment. Among the main characteristics of vehicular traffic that can be studied from speed data, we have: the intensity of circulation, vehicle speeds and travel time, origin and destination of trips, vehicular accidents, among others. The purpose of this document is to present a database of speed obtained on a road located in the Pasto (Nariño department) in southwestern Colombia. The analysed road sector has a total length of 27.5 km and is developed in a predominantly mountainous topography. The data collected corresponds to the geometric characteristics of the road, as well as the design, traffic and operating speeds of each element throughout the sector. The data series corresponds to 312 elements of the geometric design, which are located continuously along sector. The design and road speed for each element, was determined from the geometric characteristics of the road; meanwhile, the operating speed was determined using spot speed data, which was obtained from field measurements with the Bushnell Radar Gun. For the determination of the Operating Speed in each element of the layout and for each class of vehicle considered (cars, buses and two-axle trucks), in each direction of movement, a minimum sample size of 64 Spot Speed data was used. The speed dataset is of great importance, because it provides traffic and transport engineering with relevant information for investigations such as: analysis of traffic accidents, establishment of design elements, traffic operation plans, regulation and control, zones with speed problems, study of traffic flows and finally, the assignment of design speeds for similar and future projects.

Discovering the Graph-Based Flow Patterns of Car Tourists Using License Plate Data: A Case Study in Shenzhen, China

Identifying flow patterns from massive trajectories of car tourists is considered a promising way to improve the management of tourism traffic. Previous researches have mainly focused on tourist movements at the macro-scale, such as inbound, domestic, and urban tourism using flow maps. Compared with modeling the flow patterns of tourists at the macro-scale, modeling tourist flow at the microscale is more complicated. This paper takes Dapeng Island located in Shenzhen as the study area and uses the car recognition devices to collect traffic flow. Firstly, car tourists are separated from the mixed traffic flow after analyzing the spatial-temporal characteristics of tourists and residents. Next, daily graphs of tourist movements between road segments and tourist attractions are constructed. Finally, a frequent subgraph mining algorithm is used to extract the flow patterns of car tourists. The experimental results show that (1) car tourists have obvious preferences in the selection of trip time and tourist attractions; (2) the intercity tourists tend to take multidestination trips rather than a single destination trip in the same type of attractions; (3) car tourists are inclined to park their cars in an easy-to-access place, even if the attractions visited are changed. The main contribution of this paper is to present a new method for discovering the flow patterns of car tourists hidden in massive amounts of license plate data.

An Attention-Based Deep Learning Framework for Trip Destination Prediction of Sharing Bike

With the advancement of communication technology and location acquisition technology in the context of modern smart cities, the sharing bike systems offer users the great autonomy and convenience for the last/first-kilometer trip. Meanwhile, we can now able to collect, store, and analyze a large amount of sharing bike data. How to effectively use these massive data to provide better services is an emerging task. However, due to the skewed and imbalanced bike usages for stations located at different places, it is of great significance yet very challenging to predict the potential destinations of each individual trip beforehand so that the service providers can better schedule manual bike re-dispatch in advance. To address this issue, this paper proposes an attention-based deep learning framework for trip destination prediction (AFTER). AFTER first learns the low-dimension representations of users and sharing bike stations via negative sampling strategies. Then, a convolution neural network with an attention mechanism is utilized to predict the future trip destination. Experimental results on a real-world dataset indicate that the proposed framework outperforms several state-of-the-art approaches in terms of precision, recall, and F1.

Meal identity as practice - Towards an understanding of business travellers’ meal practices

Business travellers make up a large part of the customer base for the Swedish hospitality industry, accounting for 54% of the occupancy rate of Swedish hotels in 2018. Yet, very little is known about their meal habits while being at the destination of a business trip. This, even though the handling of meals in an environment that is less known to the traveller could add to the complexity of everyday life. Therefore, this study is aimed to explore actions performed by business travellers at the destination of travel as part of their meal practice with the purpose of elucidating the meal habits of this group. The research is theoretically framed within the context of social identity theory and social practice theory. A questionnaire was filled out by 538 Swedish business travellers recruited by means of self-sampling; 77% of the respondents were men, and 77% were above 45 years of age. The majority of the respondents, 67%, travelled over 50 days per annum, and 59% were located in the highest income quartile. The analysis of the data generated a general overview of the actions performed in relation to the meal, while also showing differences in actions taken based on income and gender. Women were significantly more price conscious than men and to a larger extent used technical assistance to find somewhere to eat. When travelling alone they also reported eating faster than at home and bringing back food and eat at the hotel room more often than men did. Men, in contrast, exhibited an inclination towards seeking social contexts to insert themselves in during dinners when travelling alone, as to be able to eat together with other people. The, relatively, lower income group showed more price consciousness as well as used the help of technical assistance to find somewhere to eat.

Effects of Data Preprocessing Methods on Addressing Location Uncertainty in Mobile Signaling Data

Recent years have witnessed an increasing use of big data in mobility research. Such efforts have led to many insights on the travel behavior and activity patterns of people. Despite these achievements, the data veracity issue and its impact on the processes of knowledge discovery have seldom been discussed. In this research, we investigate the veracity issue of mobile signaling data (MSD) when they are used to characterize human mobility patterns. We first discuss the location uncertainty issues in MSD that would hinder accurate estimations of human mobility patterns, followed by an examination of two existing methods for addressing these issues (clustering-based method and time window–based method). We then propose a new approach that can overcome some of the limitations of these two methods. By applying all three methods to a large-scale mobile signaling data set, we find that the choice of preprocessing methods could lead to changes in the data characteristics. Such changes, which are nontrivial, will further affect the characterization and interpretation of human mobility patterns. By computing four mobility indicators (number of origin–destination trips, number of activity locations, total stay time, and activity entropy) from the outputs of the three methods, we illustrate their varying impacts on individual mobility estimations relevant to location uncertainty issues. Our analysis results call for more attention to the veracity issue in data-driven mobility research and its implications for replicability and reproducibility of geospatial research.

STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities

AbstractClustering on spatiotemporal point events with multiple types is an important step for exploratory data mining and can help us reveal the correlation of event types. In this article, we present an effective method for discovering spatiotemporal multi‐type clusters containing events with different densities and event types (STME). Particularly, the type of events in a cluster can be different, and clusters with similar densities but different internal compositions should be distinguished. We use the distance to the kth nearest neighbour to define the size of the searched neighbourhood, and expand clusters by the concept of cluster reachable, ensuring that the proportion of various types of events in the cluster remains stable. The concept of clustering priority is also proposed to make the cluster always expand from the region with the highest density, which improves the robustness of clustering. Moreover, the density of multiple types of events in clusters is estimated to discover the internal structure of clusters and further explore the correlation between events. The effectiveness of the STME algorithm is demonstrated in several simulated and real data sets, including points of interest data in Beijing and the origins and destinations of taxi trips in New York.

Do commercial vehicles cruise for parking? Empirical evidence from Seattle

Parking cruising is a well-known phenomenon in passenger transportation, and a significant source of congestion and pollution in urban areas. While urban commercial vehicles are known to travel longer distances and to stop more frequently than passenger vehicles, little is known about their parking cruising behavior, nor how parking infrastructure affect such behavior.In this study we propose a simple method to quantitively explore the parking cruising behavior of commercial vehicle drivers in urban areas using widely available GPS data, and how urban transport infrastructure impacts parking cruising times.We apply the method to a sample of 2900 trips performed by a fleet of commercial vehicles, delivering and picking up parcels in Seattle downtown. We obtain an average estimated parking cruising time of 2.3 min per trip, contributing on average for 28 percent of total trip time. We also found that cruising for parking decreased as more curb-space was allocated to commercial vehicles load zones and paid parking and as more off-street parking areas were available at trip destinations, whereas it increased as more curb space was allocated to bus zone.

Analysing the Effects of Land Use on the Choice of Intra-Zonal Trip Destinations – A Comparison Between Weekday and Weekend Travel

Among the studies on the land use – travel relationship, few investigated it regarding weekend travel and destination choice. This study accordingly evaluates how the land use - destination choice relationship differs between weekdays and weekends using two multinomial logistic regression models in which the destination is classified into three types: microzone inside, microzone outside - macrozone inside, and macrozone outside. Major findings are that the choice of automobile alternatives for travel and their ownership are associated with the choice of the microzone inside while employment and income contribute to external trips. Among land use variables, nighttime population density turns out to be the only land use variable that consistently increases internal trips in all cases, regardless of the zone size and weekday - weekend difference, whereas daytime population density does not become significant in any case. Also, land use entropy and street connectivity are found to discourage a trip that moves from the microzone to the macrozone and transit system variables to facilitate a trip that goes beyond the microzone. Particularly, between two types of transit system variables, the choice of the microzone is likely to be associated with low bus stop density on weekdays and low metro station density on weekends.

Do rural older adults take fewer trips than their urban counterparts for lack of a ride?

Aging and the presence of one or more illnesses result in limited travel for many adults age 65 and over. Yet, the need to get to essential, social, and non-emergency medical destinations endures. At some point in their life, older adults become dependent on family/friends, or rely on for-profit/not-for-profit transportation services for their mobility needs, while some do not go on certain trips. Researchers have studied out-of-home activity and mobility of older adults using data on trips taken. There is a gap, however, in understanding trips not taken in the older adult population in rural versus urban locations. Our objectives in this paper are: (i) to investigate unmet travel needs of older adults by relying on responses for trips not taken; (ii) to examine how personal abilities, living situation, and socio-demographic factors are associated with trips not taken to various destinations; and (iii) to compare the likelihood of trips not taken due to lack of a ride in urban versus rural locations across the age and income spectrum. Our data come from a phone survey conducted across the province of Alberta, Canada, in 2017–18 (n = 1390). We specify ordinal logistic models where the dependent variable is how often a respondent did not undertake a trip due to not having a ride to various trip destinations. We find that rural seniors are more likely to not take trips compared to older adults in cities, holding all else equal including driving cessation, worsening health, and disability. Rural seniors who live alone or in low-density housing are also more likely to not take trips compared to urban older adults. Household income, however, tempers these location preferences. Our findings suggest that rural older adults can be supported through income transfers, community-based low-cost travel, and moving to higher-density residential locations.

Probabilistic model for destination inference and travel pattern mining from smart card data

Inferring trip destination in smart card data with only tap-in control is an important application. Most existing methods estimate trip destinations based on the continuity of trip chains, while the destinations of isolated/unlinked trips cannot be properly handled. We address this problem with a probabilistic topic model. A three-dimensional latent dirichlet allocation model is developed to extract latent topics of departure time, origin, and destination among the population; each passenger’s travel behavior is characterized by a latent topic distribution defined on a three-dimensional simplex. Given the origin station and departure time, the most likely destination can be obtained by statistical inference. Furthermore, we propose to represent stations by their rank of visiting frequency, which transforms divergent spatial patterns into similar behavioral regularities. The proposed destination estimation framework is tested on Guangzhou Metro smart card data, in which the ground-truth is available. Compared with benchmark models, the topic model not only shows increased accuracy but also captures essential latent patterns in passengers’ travel behavior. The proposed topic model can be used to infer the destination of unlinked trips, analyze travel patterns, and passenger clustering.

Hexagonal Zones in Transport Demand Models

Planning the development of transport systems, as well as assessing the effects of investment activities in the field of spatial development requires the use of appropriate IT tools enabling an objective assessment of investment intentions. In the field of transport analysis, one such tool is a transport demand model. Reproduction of transport-related processes is the main role of such transport demand model. This applies to both the transport of people and goods, and includes both residential travel and visitors travelling to and within the study area. The description of the process of creation and implementation of transport demands is usually based on the assumptions in the field of places of generation and absorption of travel - i.e. sources and destinations of travel. The generalization of the mathematical description of this phenomenon introduced the concept of transport zones, which are separated homogeneous areas of the study area, as sources and destinations of trips. Practice in the construction and use of transport models indicates that the problem of defining transport zones requires further investigation. Increasingly extensive transport infrastructure data collected in open databases (such as OpenStreets) are encouraging a change in the approach to the problems of constructing transport zones. The current solutions are characterized by a high level of generalization of sources and destinations rather than detailed transport analysis. This article presents the author’s method of dividing the study areas into transport zones based on a uniform hexagonal system, explaining the basic assumptions and evaluating the pros and cons of this proposed system.
 Keywords: Transport, Demand models, Algorithms

Topological properties of bus transit networks considering demand and service utilization weight measures

We present a complex weighted network analysis of a bus transport network. We model the network as graphs in L-space and P-space and evaluate the statistical properties in unweighted and weighted cases. The weights considered include number of overlapping routes and passenger demand between two bus stops. We also introduce a new supply-based edge weight called Service Utilization Factor (SUF) and define it as the passenger demand per service between two stops. We extract the origin and destination of the passenger trips from bus ticket information. In the bus system under study, the tickets are issued between ’stages’ instead of between bus stops. We propose a points of interest based procedure to map the passenger demand between stages to between bus stops. The network has scale-free behaviour with preferential attachment of nodes and is similar to small-world networks. It is well-connected with an average of 1.4 transfers required to travel between any two points in the network. We identify redundant routes in the network from strength values in the SUF weighted networks. Disassortativity in the demand weighted network indicated that bus stops with higher attractiveness are not necessarily situated contiguously along a route. The SUF weighted network is assortative indicating presence of well-serviced stops along the same the route. We identified the stops with higher betweenness centrality, that can be ideal candidates for being developed as hubs. Our conclusions exemplify the importance of considering route overlaps, passenger demand and service utilization in bus network analysis and for building an efficient bus network.

Ridesharing and motor vehicle crashes: a spatial ecological case-crossover study of trip-level data

BackgroundRidesharing services (eg, Uber, Lyft) have facilitated over 11 billion trips worldwide since operations began in 2010, but the impacts of ridesharing on motor vehicle injury crashes are largely unknown.—MethodsThis...

Analisis Karakteristik Pengguna Bus BSD Link Untuk Desain Koridor Baru Menggunakan Model Permintaan Perjalanan

Bumi Serpong Damai (BSD) is one of self contained city in Jabodetabek district. Sinarmas as developer of BSD started to develope the Mass Rapid Transit which special operated on that. Concept of MRT has been operated in the form of Bus Rapid Transit. A new BRT corridor implementation need a Passenger Demand Prediction . Thus, a special BRT Passenger Demand Modelling Method for a new BRT Line need to be developed. This attempt needs knowledge on the Existing BRT User Trip Characteristics. BSD Link User Trip Characteristics Survey were executed on Corridor Sector 1.3 Greenwich Park, during morning peak hour. The result indicates that the BRT are used mainly for working (63%) and schooling (37%) trips, the BRT passengers are the previous public transport (45%) and motorcycle (55%) users. The trip origin zone is extended 1 km to the left and right of the BSD Link corridor, while the trip destination zone is extended 500 meters to the left and right of the BSD Link corridor. The embarking connecting trip modes are dominated by motorcycle (43%) and walking trip (57%), while the alighting connecting trip modes are dominated by motorcycle (16%) and walking trip (84%)

An analysis of urban public transportation in Yogyakarta: case of Trans Jogja Bus

Transportation is the movement of humans or animals, goods, and information from one location to another. In other words, the action of transport is defined as a particular movement of an organism or thing from a point. Transportation is a derived demand that follow all basic human needs. The more populated of certain area, the more intensively of transportation, and it triggers the transport congestion. Public transportation is one solution to minimize congestion problems due to the large number of private vehicles. This study aims to (1) identify potential users of the Trans Jogja Bus; and (2) analyze the level of Trans Jogja bus occupancy. This study use quantitative research method, i.e. survey method, by using questionnaire instruments for conducting structured interviews and check lists for conducting observations. The mechanism for obtaining data in this study are by collecting primary and secondary data. The location of the research is Yogyakarta City and its surroundings which are covered by Trans Jogja Bus routes. The data was processed with SPSS 19.0 software and analyzed by quantitative descriptive analysis techniques. Results of this research shows that: (1) all of Trans Jogja Bus route actually have potential trip generation, as well as trip destination, however; (2) the occupancy level of Trans Jogja Bus are still little bit low even though service level is quite good.

Land use inference from mobility mobile phone data and household travel surveys

The mobility data derived from mobile phones may provide hints regarding land-use. Activity zones, be residential or productive, feed the global mobility once acting as origin and/or destination of trips. This research presents an approach to characterise the predominant activity of the sectors of a case of study, the metropolitan area of Malaga (Spain), using mobility patterns. The methodology is tested and compared with the socio-economical information provided by the Official General Statistics and Economic Information in order to quantify the reliability of the approach.

Identification of various transport modes and rail transit behaviors from mobile CDR data: A case of Yangon City

Abstract Developing countries worldwide are increasingly experiencing rapid urbanization and modernization, including Myanmar. Mobile call detail records provide new opportunities to measure transport demands for transportation planning. This study aims to identify mobile phone users’ transport modes and mode-transfer behaviors based on their call activities. Daily origin–destination trips were generated to detect various transport modes based on their travel speeds, which were measured for each transport mode based on ground truth data collection using a global positioning system. When the travel speed is within the predefined range for a transport mode, the traveler can be considered to be using this transport mode. The results were validated by the Person Trip Survey for Comprehensive Urban Transport Plan of the Greater Yangon. This study also analyzed the mobile phone users’ mode-transfer behaviors in Yangon. These results will be useful for predicting travel demands in improving the transportation network and public facility management.

Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin–destination (O–D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance–decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.

ANALISIS TARIKKAN PERGERAKAN PENUMPANG ANGKUTAN PENYEBERANGAN PELABUHAN ULEE LHEUE KE ZONA PERDAGANGAN DAN JASA DAN ZONA PERKANTORAN DAN PENDIDIKAN

The city of Sabang is one of the most iconic tourism spots in Aceh Province. The Ulee Lheue Port is one of the most important accesses to Sabang, which is located in Banda Aceh City, the capital of Aceh Province. So that crossing activities carried out by both local residents and tourists will always pass through Banda Aceh. Along with the developments in each of these regions, the travel demand in a form of increased movement activity has been rising. The purpose of this study is to get a trip attraction model of Ulee Lheue Port ferry boat passenger that attracted to two destination zones in Banda Aceh by using multiple linear regression analysis methods and calculated by using SPSS software based on socio-economic and travel behavior data as independent variables (X) and the movement frequency as dependent variable (Y). Based on the analysis results, the model with each trip destination zone has a different travel behavior. The most influential variables for trip attraction model of the Ulee Lheue Port ferry passengers trip to trade and service zone are work activity (X12D), gender (X1), and income (X3). The most influential variables for the trip production model of the Ulee Lheue Port ferry passengers trip to office and educational zone are government workers (a type of work) (X6C), age (X2), gender (X1), crossing time (X11), private employees (a type of work) (X6D).

Effects of neighborhood environment, safety, and urban amenities on origins and destinations of walking behavior

ABSTRACT Walking increases physical activity and reduces obesity. Walking behavior is influenced by many different factors, including urban safety. This article studies origin and destination choices of walking behavior in Salt Lake City using GIS and statistical analyzes.We find that the origins, and destinations are influenced by different neighborhood environmental factors. Street connectivity and public parks increase the number of destinations, but have little impact on the number of origins. The number of origins increases in commercial areas with compact design. In terms of safety, we find that walking behavior is influenced by robbery events rather than traffic accidents. Furthermore, a geographical weighted regression model suggests that bus stops and population density have strong positive effects in high-density areas such as the downtown areas. Our study suggests that the current analytical framework on walkability is more suitable to explain the spatial distribution of walking trip destinations than the origins.