Travel Patterns Of Users Research Articles

Differential Pricing Strategies for Airport Shuttles: A Study of Shanghai Based on Customized Bus Ticketing Data

Airport shuttle buses, as a specialized form of bus service, serve as an economical, efficient, and sustainable transportation option for air travelers. In contrast to conventional bus services, airport shuttle bus operations exhibit more pronounced market-oriented characteristics, striving to balance extensive public transport coverage with the optimization of corporate profitability. Although these services outperform regular bus transit in terms of efficiency, they incur higher operational costs. However, existing studies on enhancing profitability and optimizing resource allocation for airport shuttle buses are inadequate. This study proposes a differential pricing strategy based on historical ticketing data. Initially, we analyze the characteristics of orders, users, and reservations within the context of customized bus operations. Leveraging the differences among various groups, we employ clustering techniques to classify seat grades and segment users. Based on the clustering outcomes, we determine distinct price elasticity values for each segment. As the strategies are developed based on seat grades, booking time, and user travel patterns, the numerical experiments indicate that the proposed differentiated pricing strategy can increase the revenue of customized public transport services by at least 41%. This strategy not only enhances the efficiency of resource allocation and service accessibility but also makes the service more financially sustainable.

A reliable optimal electric Vehicle charging stations allocation

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.

Multi-objective optimal scheduling model for shared bikes based on spatiotemporal big data

Station-free bike sharing is one of the most important short-distance means of transportation. However, with the surge in the number of shared bikes, an imbalance of supply and demand in time and space caused by the disorderly parking of shared bikes has also emerged. This research combined massive spatiotemporal trajectory data of shared bikes and user travel demands to propose a feasible multi-objective optimal scheduling method. Specifically, this research presented a model that utilizes extensive order data to analyze user travel patterns, divides shared bike operation areas into internally connected communities by Geohash coding, and analyzes the shared bike dispatch hotspots in each community segment. Then, a novel model for multi-objective optimal scheduling of shared bikes was proposed based on NSGA-II. The model takes the number of transport vehicles participating in shared bike dispatching and the actual number of dispatch points as the decision variables, and its optimization goal is to reduce the cost of dispatching shared bikes and improve the utilization rate of shared bikes. The optimization effect of the model before and after the improvement of the genetic algorithm was analyzed, and the proposed optimized Pareto front solution set and optimal scheduling routes for shared bikes were given. Some of the results are noteworthy. First, the factors that affect the scheduling cost and utilization rate of shared bikes vary, so setting the variables reasonably is helpful for improving the optimization ability of the scheduling optimization model. Second, exploring the connections between shared bikes can effectively improve the scheduling efficiency of shared bikes. The research results are of great significance for optimizing dispatching routes and formulating low-carbon shared bike dispatching strategies.

Analysis of spatial–temporal validation patterns in Fortaleza’s public transport systems: a data mining approach

Abstract Understanding the spatio-temporal patterns of users’ travel behavior on public transport (PT) systems is essential for more assertive transit planning. With this in mind, the aim of this article is to diagnose the spatial and temporal travel patterns of users of Fortaleza’s PT network, which is a trunk-feeder network whose fares are charged by a tap-on system. To this end, 20 databases were used, including global positioning system, user registration, and PT smart card data from November 2018, prior to the pandemic. The data set was processed and organized into a database with a relational model and an Extraction, Transformation, and Loading process. A data mining approach based on Machine Learning models was applied to evaluate travel patterns. As a result, it was observed that users’ first daily use has a higher percentage of spatial and temporal patterns when compared to their last daily use. In addition, users rarely show spatial and temporal patterns at the same time.

Unravelling individual mobility temporal patterns using longitudinal smart card data

The increasing availability of longitudinal individual human mobility traces enables the disaggregate analysis of temporal properties of mobility patterns. The objective of this study is to identify distinctive market segments in terms of habitual temporal travel patterns of public transport users. First, travel patterns are clustered using a K-means approach followed by grouping the resulting patterns into a small number of profiles using a hierarchical clustering method. Second, we construct user-week vectors that are then clustered using a Gaussian Mixture Model approach.We apply our clustering analysis to the multi-modal public transport system of Stockholm County, Sweden, using data from more than 3 million smart card-holders. Our clustering analysis resulted in 10 day-of-the-week patterns with their composition varying across the county. In addition, we identify the following hour-by-hour weekly profiles:’Weekly commuters', ‘Lower peaks',’Late travellers', ‘Early birds' and ’Flat curve’. The behavior represented by ’Weekday commuters' and ‘Lower peaks' is most persistent over weeks. We demonstrate how a better understanding of user travel patterns offers policy makers, service planners and providers with enhanced opportunities to understand and cater for diverse market segments, for example by means of tailored fare products.

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing operators to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas.

Perception of inconvenience costs: Evidence from seven ferry services in Norway

Transport projects are assessed mainly by cost-benefit analysis (CBA). Despite its wide use, this method has some weaknesses, such as using simulated traffic volumes as an input. These traffic volumes for future projects are estimated according to user travel patterns based on the new generalised costs (GC), which include travel time, distance, and direct costs. Nevertheless, many large transport projects dramatically change accessibility; thus, other factors could play important roles in the process. Notably, ferry replacement projects reduce travel times, eliminate waiting times, and provide a reliable and flexible connection 24/7. Some of these changes, known as inconvenience costs (IC), are not included in the GC.This study contributes to the scarce literature on the IC concept and relates IC to the factors that different users value. The results, which are based on 9,878 answers from onboard ferry surveys in 2011 and 2016, revealed that inconvenience factors, such as trip planning and the lack of accessibility to family/friends, leisure activities and a broader labour market, are important. Thus, projects that improve these factors might trigger additional changes in user travel patterns. Four partial proportional odds models were estimated to relate the inconvenience factors to the trip and the socioeconomic features of users. The results showed that the waiting time, frequency of trips via the connection, trip purpose, gender, age and income played important roles in the perception of different factors. In addition, a quantification of the differences in the perceptions among the categories of the variables was described based on the average marginal effects (AME).

Temporary user-centred networks for transport systems

The concept of Temporary User-Centred Networks (TUNs) for transport systems is introduced. Affinity in these networks is defined as the time-specific degree of equivalence between travel patterns of users in the system. TUNs reveal latent social structures typically invisible to their users, enabling circumstantial collaboration opportunities amongst them. To make TUNs explicit we quantify affinity as a combination of two measures: journey similarity and journey substitutability. In the urban public transport domain, TUNs enable the diffusion of knowledge across the system in real-time. This can assist passengers adjusting travel decisions to their preferences and objectives according to service status. An enriched Automated Fare Collection (AFC) system dataset is used to demonstrate the market potential of TUNs in the urban public transport domain.

Travel Patterns of Disabled Persons Using Special Transport Systems : Case of Gyeongsangnam-do

2005년 "교통약자의이동편의증진법"의 제정이후, 교통약자의 이동권 보장을 위한 특별교통수단의 도입과 운영이 각 책임 지자체의 주도로 이루어져왔다. 특별교통수단을 운영함에 있어 이용자 통행특성은 서비스 수준과 운영 효율성 제고를 위한 필수적인 기초 자료이나, 현재까지 통행 특성에 대한 지식은 자료부족으로 인해 매우 미미한 상황이다. 본 연구는 경상남도 콜센터 자료를 활용하여 특수교통수단 이용자의 통행특성 파악을 시도하였다. 콜센터는 서비스 제공 과정에서 통행특성 파악에 필요한 다양한 자료를 산출한다. 구체적으로 이용건수, 이용자 장애 특성, 이용일자 및 시간, 휠체어 이용여부, 통행목적, 출발지 및 목적지 등을 포함하고 있다. 따라서 수단 이용자의 기본 특성 뿐 아니라 시 군 경계를 초월하는 광역통행을 포함한 O-D의 파악도 가능하다. 분석 결과 일반통행패턴과 다르거나 의미 있는 패턴이 파악되었다. 예를 들면, 휠체어 이용자는 42% 정도이며, 통행이 주로 비첨두시간에 집중됨도 파악되었다. 본 연구의 결과는 경상남도 뿐 아니라 타 지자체의 특별교통수단 정책수립과 운영을 위한 유용한 기초자료로 활용될 수 있을 것으로 기대하며, 향후 콜센터 정보수집내용에 일반적 특성을 추가할 경우 전체 자료를 활용하여 주기적인 패턴의 파악이 가능할 것으로 판단된다. Since 2005, when "The Mobility Enhancement for the Mobility Impaired Act" was enacted, special transport systems(SPS) has been introduced by each responsible local entity. For its efficient operations and service enhancements, a clear understanding of travel patterns of SPS users is required. Yet we currently have a very limited understanding about them due to a lack of necessary data. This study represents an attempt to provide a better understanding of SPS user's travel patterns with the data generated by Gyeongsangnam-do SPS Call Center. The data include the number, time and day of calls, origins and destinations of callers, types of callers' impairement etc. The data thus allow one to analyze users' travel patterns, including area-wide O-D patterns. There were a number of interesting findings. For example, wheelchair users are only about 42% and the trips are made mostly on non-peak daytime periods. The results are expected to provide a helpful information not just for Center's SPS operations, but for other local entities that are interested in developing similar call centers as well. By refining the SPS system, periodic patterns of callers could be identified in the future.

Generating Route-Specific Origin–Destination Tables Using Bluetooth Technology

Bluetooth media access control (MAC) data collection technology has emerged as a forerunner in the suite of passive data collection techniques used in travel time and origin–destination (O-D) data collection efforts because of its low cost, ease of implementation, and richness of the resultant data set. With a few notable exceptions, the majority of published research and practical applications of Bluetooth MAC data to date have focused on travel speed analysis. This paper aims to add to the existing literature on Bluetooth O-D data research by describing an analytic approach used to develop route-specific O-D tables for a 15-mi corridor in Jacksonville, Florida. The data from this project were successfully used as an after-model validation tool for base and future year toll revenue forecasts. Bluetooth MAC data were shown to provide a robust and rich data set capable of providing insight into the travel patterns of users in a corridor that would not be easily or efficiently achieved with other data collection methods. The paper also seeks to provide practical guidance and insight into successfully deploying and analyzing Bluetooth MAC data.

Road Network System in Port Klang, Malaysia and Impacts to Travel Patterns

Road network system is one of the most important infrastructures in any country as it provides the means to move people and goods. Inefficient road network system will disrupt the transportation movement, environment and affect human movement. This paper examines the structure and patterns of the road network system in Port Klang, Malaysia. It found that after implementation of toll on highways, heavy lorries decreased their uses of the tolled highway, and started using regular roads. The building and charging of tolls on the new highway has impacted the travel patterns of users in the area.

Automated Work Zone Information System on Urban Freeway Rehabilitation: California Implementation

In October 2004, about 9 lane km of deteriorated truck lanes on Interstate 15 at Devore in Southern California was rebuilt during 18 days with extended one roadbed full-closures with the counterflow traffic system and 24-h construction operations. The project implemented the automated work zone information system (AWIS) to reduce peak hour delay during construction by changing road user's travel patterns and diverting traffic to detour routes. This paper describes the design, performance, and validation of AWIS with monitored traffic data before and during construction used. The AWIS was installed to provide road users with real-time travel information so that they could avoid traffic delays in the construction work zone (CWZ) corridor. Travel times through the CWZ were estimated from speed data and enhanced in two ways: (a) portable and permanent changeable message signs on site and (b) off-site (project website) implementation with travel time messages, traffic snapshots, and video streaming. AWIS trave...

Automated Work Zone Information System on Urban Freeway Rehabilitation

In October 2004, about 9 lane km of deteriorated truck lanes on Interstate 15 at Devore in Southern California was rebuilt during 18 days with extended one roadbed full-closures with the counterflow traffic system and 24-h construction operations. The project implemented the automated work zone information system (AWIS) to reduce peak hour delay during construction by changing road user's travel patterns and diverting traffic to detour routes. This paper describes the design, performance, and validation of AWIS with monitored traffic data before and during construction used. The AWIS was installed to provide road users with real-time travel information so that they could avoid traffic delays in the construction work zone (CWZ) corridor. Travel times through the CWZ were estimated from speed data and enhanced in two ways: ( a) portable and permanent changeable message signs on site and ( b) off-site (project website) implementation with travel time messages, traffic snapshots, and video streaming. AWIS travel time was estimated at 90% of actual travel times as measured by probe vehicles. The peak hour traffic demand through the CWZ was reduced by up to 18%, with significant volume increases to detour freeways. The AWIS operation contributed to a decrease in the maximum delay for weekday peak hours (from expected 90 min to measured 50 min on average during construction). The outcome of this study will help transportation agencies and practitioners efficiently design and operate AWIS for highway rehabilitation under high traffic volume.

Modeling Interaction Between Internet Communication and Travel Activities: Evidence from Bay Area, California, Travel Survey 2000

Increasingly, advanced information technologies affect both activity and travel participation. Although several studies have focused on specific activities such as e-shopping or telecommuting, this study focuses on the relationship between physical and virtual activity participation in maintenance and discretionary activities (specifically using Internet technologies). In particular, three related dimensions are investigated using a series of statistical models: ( a) factors affecting information and communications technology (ICT) use and the propensity to perform virtual activities using the Internet, ( b) the relationship between physical and virtual activity participation and ICT use, and ( c) interactions among ICT use, activity attributes, and observed travel behavior. The models are estimated by using recent empirical activity-diary data from the San Francisco Bay Area (California). The results provide considerable evidence in support of substitution and generation of trips due to ICT use (particularly the Internet). The results highlight the influence of technological familiarity, work-related attributes, time constraints, timing of travel, mobility and connectivity needs, and sociodemographic factors on the interactions between ICT use and travel patterns of users. These results have important implications for travel demand estimation and forecasting given the growing adoption and use of ICT among various segments of the population.

Katy Freeway High-Occupancy Vehicle Lane Value Pricing Project, Houston, Texas: Evaluation of Usage

Usage of the QuickRide program on the Katy high-occupancy vehicle (HOV) lane in Houston, Texas, is described. The QuickRide program allows two-person carpools to use the HOV lane for $2.00 during peak periods when the lane is restricted to three or more persons. Use of QuickRide during its first year is described, and an analysis of the demand for the program is presented. QuickRide usage, reported travel behavior, and demographic data are used to analyze user travel patterns, travel time savings, and frequency of use. In the 1-year demonstration, demand averaged slightly over 100 vehicles per day, with more than 60 percent of these vehicles traveling in the morning peak. Participants' average use of QuickRide was only about 0.9 times per week, and very few participants used it more than five times per week. Yet a sampling of travel days indicates that, for the $2.00 fee, the average vehicle saves about 20 min of travel time. Responses to a mail-back survey show a significant mode shift from drive-alone to QuickRide, amounting to more than 50 percent of QuickRide trips. A substantial shift was also seen in the time of travel into the peak hour, totaling about one-third of QuickRide trips. Finally, larger household sizes and higher incomes appear to be good predictors of QuickRide use. Interestingly, previous use of the HOV lane was not a good indicator, either positively or negatively, for the frequency of use of QuickRide. These results suggest that ( a) the total demand for HOV-2 value pricing may be limited in major travel corridors, despite large potential time savings; ( b) substantial shifts in mode and time of travel are possible with HOV-2 value pricing; and ( c) household size and income are good indicators, but HOV lane use is a poor indicator, of the frequency of use of an HOV-2 value pricing program.