Trip End Research Articles

Optimisation of trip end service for international container sea-rail intermodal transportation – a case of China–Korea container railway ferry

ABSTRACT An innovative transportation mode for exporting containers from Korea to Europe, involving a combination of port-access liner train, Korea–China train ferry, and CR-Express train, is presented with the objective of maximising transportation profits of the intermodal system, A bi-level model is built, in the upper level, the model determines the location and departure frequency of port-access liner train in Korea, as well as the departure frequency and vessel size of Korea–China train ferry, while in the lower level, the model calculates the choice probabilities of the paths taken by the containers and the corresponding link traffics. A survey is conducted, including both RP (revealed preference) and SP (stated preference) surveys on containers’ path choices, for calibrating the transport utility function by fusing the RP and SP data. The case study shows that for the port-access train, it is optimal to locate the terminals in Seoul and Busan, and to dispatch 11 and 1 trains per week, respectively.

Dynamic ride-sharing impacts of greater trip demand and aggregation at stops in shared autonomous vehicle systems

Sharing vehicles and rides may become the norm with public use of fully-automated self-driving vehicles in the near future, assuming pandemic-related health concerns fade. Dynamic ride-sharing (DRS) or ride-pooling of trips can significantly improve system performance by lowering empty vehicle-miles traveled (eVMT) and increasing average vehicle occupancy (AVO). With several cities looking to promote efficient curb space use, especially with the use of pickup and drop-off locations (PUDOs), this study explores the impacts of PUDOs on DRS rates and AVO values. Various PUDO spacings and trip-demand densities were studied, across the Bloomington, Illinois region, using the agent-based simulator POLARIS. Results reveal that greater PUDO spacing or distances between stops and higher levels of SAV use or trip demand increase AVO (by up to 20% per 4-seater SAV, on average) and decrease SAV VMT (by up to 27%) compared to door-to-door SAV fleet operations without DRS or PUDOs. A quarter-mile PUDO spacing is recommended in downtown regions, similar to current transit stop spacing, to keep walking trips short and demand relatively high. At 0.25 mi PUDO spacings (thoughtfully placed, using origin and destination clusters), travelers walked less than 5 min at either trip end, on average, while 0.5 mi spacings led to another 1 min (approximately) of walking. More evenly distributed and higher SAV demand can save up to 39% total VMT from use of DRS and PUDO stops. It is also important to prepare for queuing areas at PUDOs in settings of high trip density, to limit curbside congestion.

Breathing Fresh Air into Diabetes Education: A Qualitative Study

Type 1 diabetes (T1D) is an endocrine and metabolic disorder affecting approximately 1.4 million adults living in the United States. Psychosocial factors have been identified as key barriers to improvements in self-care among T1D adults, including emotional well-being, social support and self-efficacy, and personal motivation. It is posited that nonclinical approaches, such as peer-based outdoor adventure education programs, could assist T1D adults with their individual psychosocial needs. However, very little research exists to support this unique approach to care. The purpose of this study was to explore the meaning that adults living with T1D ascribe to participation in a wilderness backpacking trip. Eighteen participants with T1D completed a 5-day backpacking trip on the Chilkoot Trail in Alaska and British Columbia. Once on the trip, participants were invited to participate in two semi-structured interviews within 48 hours of the trip start and end date. Using a phenomenological approach, three themes were constructed: a) seeking and finding a diabetes community, b) revealing capabilities to yourself and the world, and c) enhancing diabetes management and self-care on the trail and in life. A common thread across the three constructed themes was the sense of belonging to the diabetes community, which was described by the participants as paramount to their positive overall trip experience. This finding extends assertions that peer relationships during OAE programs may play a central role in improving psychosocial functioning for individuals living with T1D. This provides evidence on the power of using outdoor recreation to positively impact the community of individuals with T1D.

Improved F-DBSCAN for Trip End Identification Using Mobile Phone Data in Combination with Base Station Density

Trip end identification based on mobile phone data has been widely investigated in recent years. However, the existing studies generally use fixed clustering radii (CR) in trip end clustering algorithms, but ignore the influence of base station (BS) densities on the positioning accuracy of mobile phone data. This paper proposes a new two-step method for identifying trip ends: (1) Genetic Algorithm (GA) is utilized to optimize the CRs of DBSCAN under different BS densities. (2) We propose an improved Fast-DBSCAN (F-DBSCAN) for two objectives. One is for improving identification accuracies; the parameter CRs for judging core points can be dynamically adjusted based on the BS density around each mobile phone trace. The other is for reducing time complexity; a fast clustering improvement for the algorithm is proposed. Mobile phone data was collected by real-name volunteers with support from the communication operator. We compare the identification accuracy and time complexity of the proposed method with the existing ones. Results show that the accuracy is raised to 85%, which is approximately 6% higher than the existing methods. Meanwhile, the median running time can be reduced by about 76% by the fast clustering improvement. Especially for noncommuting trip ends, the identification accuracy can be increased by 8%. The average identification errors of travel time and trip end coordinates are reduced by about 12 min and 321 m, respectively.

Trip end identification based on spatial-temporal clustering algorithm using smartphone positioning data

As a widespread portable probe, smartphone equipped with Global Navigation Satellite System (GNSS) can continuously track individual’s travel trajectory, it is potential for trip end information identification. Existing studies mostly use rule-based methods with specific dwelling time and distance thresholds for trip end detection, these methods are highly dependent on expert experience and lack universality across different traffic environments. Some density-based spatial clustering methods also have issues in the case of GNSS signal missing, traffic congestion, short-time stays and repeat visits to the same place etc. Therefore, this paper proposes a two-step method for trip end identification by using smartphone GNSS positioning data. First, a spatial-temporal density-based clustering algorithm (ST-DBCA) is proposed for trip end identification, the method considers both spatial and temporal travel trajectory point density at the same time, and performs much better than traditional clustering methods. Second, three optimization models are further proposed to optimize the identification results, including 1) a short time stay optimization model, 2) a redundant stay optimization model, and 3) a traffic congestion stay optimization model. Field tests in Chengdu China are conducted to verify the feasibility and effectiveness of the proposed methods. Results show that the average trip end identification accuracy under different trip purposes reaches 92.8%, and the average errors of arrival and departure time are smaller than 150 s.

Evaluation of methods to distinguish trips from activities in walking and cycling GPS data

An abundance of GPS data on walking and cycling requires substantial data processing – a primary component of which is trip identification to distinguish between data recorded during travel versus activities. The objective of this paper is to systematically evaluate trip identification algorithms in the literature and provide recommendations to improve performance for walking and cycling trips. Fourteen algorithms are applied to 1685 GPS trajectories from Vancouver, Canada, and evaluated on the bases of their agreement, distinction of trip and activity data characteristics, processing time, and accuracy (based on a labeled subset). Error sources are identified in relationship to trajectory, network, and weather factors. Results indicate poor concordance and widely varying performance, with no algorithm best across all measures. Four high-performing algorithms are identified with at least 90% record-level accuracy; other considerations include accuracy of the inferred number and duration of trips, precision of identified trip end points, and computational resource requirements. Density is a key variable for trip identification in the best-performing algorithms. Proximity to tree canopy, buildings, bridges, and tunnels affects the accuracy of some algorithms more than others. Most algorithms err almost entirely with false trips or false activities, which is a bias of concern for analysis. The importance of trip identification decisions should motivate more thorough reporting to enhance reproducibility and reliability.

Towards the Inference of Travel Purpose with Heterogeneous Urban Data

In people’s daily lives, travel takes up an important part, and many trips are generated everyday, such as going to school or shopping. With the widely adoption of GPS-integrated devices, a large amount of trips can be recorded with GPS trajectories. These trajectories are represented by sequences of geo-coordinates and can help us answer simple questions such as “where did you go”. However, there is another important question awaiting to be answered, that is “what did/will you do”, i.e., the trip purpose inference. In practice, people’s trip purposes are very important in understanding travel behaviors and estimating travel demands. Obviously, it is very challenging to infer trip purposes solely based on the trajectories, because the GPS devices are not accurate enough to pinpoint the venues visited. In this paper, we infer individual’s trip purposes by combining the knowledge from heterogeneous data sources including trajectories, POIs and social media data. The proposed Dynamic Bayesian Network model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DBN</i> ) captures three important factors: the sequential properties of trip activities, the functionality and POI popularity of trip end areas. In addition, we propose an efficient method with local candidate pools to identify POIs from geo-tagged social media messages, and learn the POI popularities from nearby social media data. Moreover, trip data is usually imbalanced across different activities. This data imbalance problem can cause serious challenges because the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>DBN</b></i> model could be biased by those “popular” class labels. Considering this challenge, we propose an ensemble DBN method with sampling technique ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eDBN</i> ) which results in more accurate inference. Furthermore, real-world trip data are continuously collected on a daily basis. The batch model would result in unnecessary computation because historical data need to be revisited. We handle this problem by proposing an incremental DBN method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iDBN</i> ) which is both effective and efficient. Extensive experiments are conducted on real-world data sets with trajectories of 8,361 residents and the 6.9 million geo-tagged tweets in the Bay area. Experimental results demonstrate the advantages of the proposed method on correctly inferring the trip purposes.

Identifying intracity freight trip ends from heavy truck GPS trajectories

Intracity heavy truck freight trips are basic data in city freight system planning and management. In the big data era, massive heavy truck GPS trajectories can be acquired cost effectively in real-time. Identifying freight trip ends (origins and destinations) from heavy truck GPS trajectories is an outstanding problem. Although previous studies proposed a variety of trip end identification methods from different perspectives, these studies subjectively defined key threshold parameters and ignored the complex intracity heavy truck travel characteristics. Here, we propose a data-driven trip end identification method in which the speed threshold for identifying truck stops and the multilevel time thresholds for distinguishing temporary stops and freight trip ends are objectively defined. Moreover, an appropriate time threshold level is dynamically selected by considering the intracity activity patterns of heavy trucks. Furthermore, we use urban road networks and Point-of-Interest (POI) data to eliminate long-stay temporary stops to improve method accuracy. The validation results show that the accuracy of the method we propose is 88.79%. Our method incorporates the impact of the city freight context on truck trajectory characteristics, and its results can reflect the spatial distribution and chain patterns of intracity heavy truck freight trips, which have a wide range of practical applications.

Identifying intercity freight trip ends of heavy trucks from GPS data

The intercity freight trips of heavy trucks are basic data for transportation system planning and management. In recent decades, extracting intercity freight trips from GPS data has gradually become the main alternative to traditional surveys. Identifying freight trip ends (origin and destination) is the first task in trip extraction. Although many trip end identification methods have been proposed in previous studies, most of these studies subjectively determined key parameters and ignored the complex characteristics of truck trajectory and freight activities. In this paper, we propose a data-driven trip end identification method based on massive GPS data of heavy trucks in China. First, we capture heavy truck trajectory characteristics under the influence of GPS drift to identify truck stops from GPS data. Second, we analyze the temporal characteristics of truck activities and use freight-related point-of-interest (POI) data and highway network GIS data to identify valid trip ends from truck stops. The results of method validation suggest that the accuracy of our proposed method is significantly improved in comparison with the benchmark methods. We further extract intercity freight trips from the identified trip ends and analyze the spatiotemporal characteristics of intercity freight trips in China.

Evaluating rail transit's comparative advantages in travel cost and time over taxi with open data in two U.S. cities

This paper presents a comparative analysis of rail transit and taxi by travel cost and time based on the large-scale taxi trip data and public transit schedule information in two major U.S. cities. To quantify the relative advantage of one mode over the other, we introduce the notion of travel gradient, which is travel cost difference divided by travel time difference. Based on the signs of travel cost and time differences, we classify all trips into four quadrants. Quadrant II trips are selected for further analysis because rail transit is identified to be competitive with taxi for such trips. We also explore the relation between various trip characteristics and travel gradient with and without considering the spatial variation of such a relation. Main research findings include: (1) around 70% of the taxi trips in the considered datasets can be substituted with rail transit trips if the maximum walking distance is 0.5 miles at each trip end; (2) for around 10% of taxi trips with both modes being viable, rail transit dominates taxi in both travel cost and time; for the rest, rail transit is competitive with taxi; (3) the marginal travel cost saving due to mode switching from taxi to rail transit is about $70; and (4) there exist clearly spatial variations of the relation between trip characteristics and travel gradient. The main policy recommendation from this study is that rail transit can be better marketed by highlighting its relative advantage over taxi in travel time and cost, especially for travels in certain directions and time periods.

Mining dockless bikeshare data for insights into cyclist behavior and preferences: Evidence from the Boston region

• Studied cyclists’ behavior using en route GPS records from a dockless bikeshare system. Commercial areas have excess origins, while residential areas have excess destinations. • Cyclists often use sidewalks, other footpaths , and parking lot drives. • 36% of trips involve contraflow on a one-way segment. • Due to underdeveloped bike network, only 7% of trips are 100% on low-stress links. Emerging micromobility services provide not only new transportation options, but also valuable sources of data for researchers and planners seeking to understand traveler behavior and preferences and to improve transportation networks. Dockless systems for shared bicycles and electric scooters have recently begun operating in many American cities, providing data not only on trip start and end, but also on the route followed. In this study, we analyze data from more than 110,000 dockless bikeshare trips in Boston’s suburbs. About 15% of trip ends were within 100 m of a transit station, indicating that bikeshare serves many more functions than access to transit. More than 40% ended in a town or village center, suggesting that bikeshare may support the local economy. From examining GPS traces, it was clear that – contrary to the assumptions of common bike routing algorithms – bike riders often use sidewalks, other footways and soft-surfaced paths, parking lots, and driveways, and 36% ride the wrong way on one-way streets. An examination of the streets with high contraflow volumes indicates two common profiles. One is streets whose travel channel is so narrow that either the bike or vehicle has to pull into a parking lane to let the other pass; those streets typically have low traffic speeds and very low traffic volumes. The other is streets whose travel channel is wide enough for a bike and car to pass without either yielding to the other; they typically have greater vehicular speed and volume, but still only one lane. After classifying streets by level of traffic stress, we find that only 7 percent of trips use exclusively low-stress links, and that about 40 percent of bike-miles are ridden on low-stress links. These low percentages are consistent with an underdeveloped bike network that forces riders to use through streets for all except ultra-local trips; it may also reflect a prevalence of sidewalk riding on major arterials.

Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data

Cellular phone data has been proven to be valuable in the analysis of residents’ travel patterns. Existing studies mostly identify the trip ends through rule-based or clustering algorithms. These methods largely depend on subjective experience and users’ communication behaviors. Moreover, limited by privacy policy, the accuracy of these methods is difficult to assess. In this paper, points of interest data is applied to supplement cellular phone data’s missing information generated by users’ behaviors. Specifically, a random forest model for trip end identification is proposed using multi-dimensional attributes. A field data acquisition test is designed and conducted with communication operators to implement synchronized cellular phone data and real trip information collection. The proposed identification approach is empirically evaluated with real trip information. Results show that the overall trip end detection precision and recall reach 95.2% and 88.7% with an average distance error of 269 m, and the time errors of the trip ends are less than 10 min. Compared with the rule-based approach, clustering algorithm, naive Bayes method, and support vector machine, the proposed method has better performance in accuracy and consistency.

Reviewing trip purpose imputation in GPS-based travel surveys

Abstract The global positioning system (GPS) has motivated rapid advances in mobility data collection. A massive amount of spatio-temporal information has made it possible to know where a person was and when, but not how and why (s)he travelled, creating the need for inference models. Compared with mode detection, purpose imputation has been insufficiently studied. However, the relative lack of attention to purpose identification does not mean that this field has not emerged. For this paper, which is the first review dedicated to inferring trip purposes from GPS data, 1162 non-duplicate papers from four databases (Scopus, Web of Science, ScienceDirect and TRID) were screened, and a corpus of 25 publications was selected for examination. Based on these papers, the purpose imputation problem is defined in the contexts of the evolution of GPS-based travel surveys and two research domains, transportation science (TS) and human geography (HG). Subsequently, three steps of the purpose detection process, namely trip end detection, input feature selection and main algorithms and validation, are surveyed. During these procedures, the differences between studies in TS and those in HG are highlighted. Finally, unresolved issues related to data and feature selection, algorithms and assessment are discussed substantially to provide potential research directions. This review may be an informative reference for those newly accessing the GPS-based purpose imputation field and/or intending to develop solutions to this problem.

Spatial associations of dockless shared e-scooter usage

In this study, we explore the usage of e-scooter sharing services in Austin, Texas over about a six-month period. The study is based on trip records of all the shared e-scooter operators in Austin and includes trip start and end locations. We use both analysis of trip patterns and spatial regression techniques to examine how the built environment, land use, and demographics affect e-scooter trip generation. Our findings show that people use e-scooters almost exclusively in central Austin. Commuting does not seem to be the main trip purpose, and usage of e-scooters is associated with areas with high employment rates, and in areas with bicycle infrastructure. People use e-scooter sharing regardless of the affluence of the neighborhood, although less affluent areas with high usage rates have large student populations, suggesting that students use this mode of travel. Implications for planners suggest that better bicycle infrastructure will facilitate e-scooter usage, college towns are a ready market for e-scooter sharing services, and e-scooters may be a substitute for some short non-work trips, reducing car usage, and benefiting the environment.

Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium

The node-place model is an analytical framework that was devised to identify spatial development opportunities for railway stations and their surroundings at the regional scale. Today, the model is predominantly invoked and applied in the context of ‘transit-oriented development’ planning debates. As a corollary, these model applications share the pursuit of supporting a transition towards increased rail ridership (and walking and cycling), and therefore assumingly a transition to more sustainable travel behavior. Surprisingly, analyses of the importance of node and place interventions in explaining rail ridership remain thin on the ground. Against this backdrop, this paper aims to integrate the node-place model approach with current insights that derive from the trip end modeling literature. To this end, we apply a series of regression analyses in order to appraise the most important explanatory factors that impact rail ridership in Flanders, Belgium, today. This appraisal is based on both geographical and temporal data segmentations, in order to test for different types of railway stations and for different periods of the day. Additionally, we explore spatial nonstationarity by calibrating geographically weighted regression models, and this for different time windows. The models developed should allow policy and planning professionals to investigate the possible demand impacts of changes to existing stations and the walkable area surrounding them.

How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area

Understanding travel behavior and its relationship with built environment is crucial for sustainable transportation and land-use policy-making. This study provides additional insights into the linkage between the built environment and travel mode choice by looking at the built environment characteristics at both the trip origin and destination in the context of transit-oriented development (TOD). The objective of this research is to provide a better understanding of how travel mode choice is influenced by the built environment surrounding both trip end locations. Specifically, it investigates the effect of transit-oriented development policy and the way it affects people’s mode choice decisions. This is accomplished by developing discrete choice models and consideration of urban form characteristics at both trip ends. Our findings not only confirmed the important role the built environment plays in influencing mode choice, but also highlighted the influence of policies, such as TOD, at both trip end locations. Results suggest that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. However, the magnitude of this TOD effect is larger at trip origin compared to destination. Higher residential and employment densities at both trips ends are also associated with lower probability of auto and higher probability of transit and non-motorized mode choices.

Objective Monitoring of Driving Behavior in Parkinson’s Disease: The Utility of the Chula Parkinson Car®

Introduction: Driving competency is important to evaluate among individuals with Parkinson’s disease (PD). Driving in natural situations is the preferred assessment method; thus, we used a naturalistic driving environment to identify driving competency among individuals with PD in comparison to healthy age-matched controls. Methods: Based on a power analysis, we recruited 20 participants (10 with PD and 10 healthy age-matched controls). Each participant completed 3 tasks while driving the ChulaPD car, a 4-door sedan installed with computerized monitoring systems. The tasks were forward and backward vehicle movement, reversing into a parking space, and parking parallel to a sidewalk. Trip start and end times, vehicle speed, and acceleration and deceleration times were logged using steering wheel motion, location parking sensors, and dashboard cameras and compared between groups. Results: Age, gender, possession of a driver’s license, present driving conditions, Thai Mini-Mental State Examination score, and driving experience did not significantly differ between groups. However, the PD group took longer to complete the driving tests (p = 0.002), had slower vehicle speeds (p = 0.002), longer brake times (p = 0.007), and decreased brake pressure ability (p = 0.009). Under normalized conditions, the ratio of failed driver’s license tests was also higher among the PD group than in the control group (70 vs. 10%, p = 0.006). Conclusions: Individuals with PD had less-than-adequate driving ability based on our naturalistic setting. Our assessment method may be useful in other populations with chronic illnesses or for older adults. We discuss how naturalistic assessments could become the standard for evaluating driving ability in Thailand and elsewhere.

Optimalisasi Kinerja Terminal Dengan Menggunakan Metode Penilaian Tingkat Kepentingan Dan Kepuasan Pengguna Terminal (Studi Kasus Terminal Poris Pelawad Kota Tangerang)

Terminal is the place for drop off and pick up of the people and transfer mode to next journey according trip end destination. The facility of terminal divided of vehicle facility, people facility, and operation facility. This facilities must be good performance, so can service the customer. But tf1e terminals officer, must be evaluation the facility conditions periodically. Importancei1erformance analysis method can be use, with interview the customer of terminal. Base on data from interview, percentage of importance level and satisfaction level can be measure. From this prercentage, problematic mapping shown for decided problem solving performance of terminal

Developing Transportation Engineering and Planning Metrics for Rural Volunteer Driver Programs

Car-based volunteer driver programs have emerged as important providers of transportation in many rural and low-density locations in which automobile dependence is high and transit access is negligible. The degree of this importance is not known, yet it can be expected that demand for these services will increase commensurate with the transportation needs of the aging population. The challenge remains that there is limited technical guidance for existing and new programs to anticipate and respond to the anticipated increases in travel demand from an aging population. It is also unclear how these programs can fit into transportation engineering and planning. This paper presents aggregate results of travel data collected for one year using a uniform data reporting approach by seven car-based volunteer driver programs in New Brunswick, Canada. Users 65 years and older accounted for 49–65% of members, and programs on average made 2.0 stops (trip end) per drive (trip chain), drove 39 km per stop, 61 km per drive, and had an occupancy of 1.5 riders per drive. A total of 88% of drives had 3 or fewer stops, and one drive was cancelled for every 10–11 offered. A total of 49.3% of all stops (excluding home) were for health purposes, with 20.7% for education/work. Seasonal, daily, and hourly trends were observed. Volunteers in groups with &lt;100 riders provided more hours/drive but fewer hours/year than those in larger groups. These data can provide a basis for a more sophisticated approach to program planning and could be useful in forecasting for future demand.

Sustainable approach to land development opportunities based on both origin-destination matrix and transportation system constraints, case study: Central business district of Isfahan, Iran

Given that there are restrictions on expanding the infrastructure for the purpose of fulfilling future transportation demand, it is critical to consider the capacity of the existing transportation system and the sustainable extent of development, in order to avoid problems such as traffic congestion and air pollution. In this study, by applying the inverse of conventional transportation planning, which is different from the land-use transportation interaction modeling, land-use limits for each Traffic Analysis Zone (TAZ) are determined as a function of the transportation system capacity. Indeed, this method is applied to examine the trip generation capacity of each TAZ with respect to its trip end values. Comparing the indicated capacity with the current demand for each zone can act as a key tool for urban policy makers and planners to define the spatial distribution of new activities based on the present capacity of the transportation system in the studied area. For this purpose, after selecting the transportation network of the central business district in the City of Isfahan as a case of study, the artificial neural networks solution is used to solve the inverse transportation planning problem.