Trip Model Research Articles

Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin

ABSTRACT Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the predictive accuracy of zone-to-zone direct demand models, they lack inference ability due to their complex model structure. Furthermore, zone-to-zone direct demand models suffer from limited sample size for machine learning due to fixed number of origin-destination (OD) pairs. To overcome these limitations, we propose a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) for direct demand modeling of zone-to-zone ride-hailing trips that captures both linear explanatory components and non-linear patterns and improves the predictive accuracy by generating data to expand the OD sample size. The proposed LR-WGAN is found to significantly improve the predictive accuracy of OD ride-hailing demand in Chicago and Austin. Furthermore, the linear explanatory component of the model is utilized to draw inferences regarding the relationship between OD ride-hailing demand and predictor variables.

Developing B2C freight trip generation model from Courier Express Parcel establishments: A case study of two Indian cities

A panoptic study of transportation planning requires an investigation of both passenger and freight transportation for the holistic growth of the concerned infrastructure. The increase in online purchases in the last few years has surcharged the courier, express and parcel (CEP) sector and the inclusion of freight trips generated by such establishments is essential while exploring urban freight transport scenarios. This paper essentially explores the different parameters which impact B2C freight trip generation and employs the ordinary least square (OLS) regression approach to model the same. A combined dataset of 104 CEP establishments from two cities in Northeast India, Guwahati and Silchar, was collected using an establishment-based freight survey (EBFS). Modelling inferences showed that delivery executives (DE), gross floor area (GFA) of the establishment, and category of clients (COC) were significant variables in forecasting B2C freight trip generation. The statistical results also showed a positive correlation between the predictors and the B2C freight trips. The estimation of the B2C freight trip model showed that CEP establishments are large freight traffic generators which will provide insight to the urban planners for policy making and r emedial measures.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.

Pengaruh Hasil Belajar Keterampilan Menulis Puisi Dengan Model Field Trip Siswa Kelas IV SD Inpres Andi Tonro Kota Makassar

The main problem in this research is that there is an influence of the field trip model on the learning outcomes of poetry writing skills for fourth grade students at SD Inpres Andi Tonro, Makassar City. This research aims to describe the influence of learning outcomes on poetry writing skills using a field trip model for fourth grade students at SD Inpres Andi Tonro, Makassar City.This type of research is quantitative research that uses experimental research (pre-experimental design) with a one-group pretest-posttest design. The population in this study were all fourth grade students at SD Inpres Andi Tonro, Makassar City with a sample size of 48 students. The research instruments used to collect data were tests (pretest-posttest) and observation sheets.T he data analysis technique used in this research uses descriptive statistical analysis and inferential statistical analysis with the t test. The results of the research prove that there is an influence on the results of learning poetry writing skills using the field trip model for fourth grade students at SD Inpres Andi Tonro, Makassar City.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

Revealing Urban Color Patterns via Drone Aerial Photography—A Case Study in Urban Hangzhou, China

Urban color, primarily emanating from building façades and roofs, plays a pivotal role in shaping a city’s image and influencing people’s overall impression. Understanding the nuances of color patterns contributes significantly to unraveling the uniqueness and identity of a city. This study introduces a statistical method for the systematic analysis of urban color and macroscopic urban structure. Specifically, we employ drones to collect and extract building roof and façade colors in the main urban area of Hangzhou, mapping these colors to the HSV color space. Subsequently, we establish a random walk model and an origin–destination trip model within the urban transportation network to simulate the movement of people. Our experiments reveal robust correlations between façade and roof values and passing frequency (with the Pearson correlations reaching 0.70). Through a rigorous statistical analysis, we gain insights into the distribution of urban color and the impact of architectural structures on color variations, identifying potential patterns or trends. By integrating color data with architectural structure data, our systematic research method deepens the understanding of the visual features that define cities. Beyond theoretical exploration, this approach offers practical insights for building planning and design. This study not only sheds light on the relationship between architectural structures and urban color but also provides valuable guidance for future urban development initiatives.

A categorization-modelling procedure to estimate urban goods transport demand with a minimum number of activity categories

This paper presents an integrated clustering-regression modelling framework to assess the number of trips that each establishment of a given urban zone, city or conurbation generates. The goal of the method is to construct establishment-based trip models using the categorial FTG methodology using a minimal set of categories of activities. The methodology combines a hierarchical cluster analysis that defines a limited number of coherent categories of activities, then a regression-based modelling method that links the FTG rates (production, attraction and generation) to employment, giving a specific functional form for each category (constant, linear, logarithmic or potential). The models are constructed and calibrated on the basis of the 2011-2012 Freight Trip Survey of the Paris Region. Using a hierarchical clustering technique, 5 categories of establishment are defined and resulting FTG models obtained. Most functional forms are linear or potential, with some particular cases where constant generation is preferred. Results are assessed using the R2 coefficient and discussed. The resulting models are robust and applicable to any French city.

Innovative History Learning through Virtual Field Trips Model

The purpose of this research is to create an innovative history learning model by using a virtual field trip model. The problem will be studied in this study are: (1) what is the essence reality in learning history? (2) how is the application of the virtual field trip model to a history learning? Research method selected for this study is a literature study where the data material needed are obtained from books, journals, encyclopedias, articles and others. According to the study result, it defines a learning history is a teaching and learning activity regarding past events with conveyed meaning for the present. Within its development process in responding to the Covid 19 pandemic, an innovative virtual field trip model was created and designed to make students able to play an active role in the history learning process which can be seen from the following syntax: (1) Determining the learning materials, (2) Guiding the group/individual investigations, (3) Presenting the result of students’ investigation and (4) Evaluation. Keywords: Learning Model, History Learning, Virtual Field Trips

Self-driving vehicles’ impacts on Americans’ long-distance domestic travel choices

ABSTRACT This research estimated models for long-distance domestic passenger trips before and after the introduction of autonomous vehicles (AVs) and their application to a 10% synthetic US population. The authors synthesized 12.1M households and 28.1M individuals across 73,056 US census tracts. To generate disaggregated passenger trips, travel demand models, including trip frequency, season, purpose, party size, mode choice, and destination choice, and vehicle ownership models were estimated. Different datasets, including a 2021 long-distance AV survey, 2016/17 National Household Travel Survey (NHTS) survey, EPA Smart Location data, and FHWA rJourney dataset were used for model estimation. The model applications indicated AV ownership to be 0.33 per capita after the introduction of AVs within the marketplace with a $3500 AV technology cost premium in the year 2040. Total person-miles traveled per capita in long-distance trips was also estimated to rise 35% (from 280 to 379 miles per month).

Stochastic location models applied to multipurpose shopping trips

In a competitive multi-purpose (MP) trips model, it is assumed that the proportion of customers that do MP trips is given. In this paper, we investigate a model with a stochastic proportion of MP trips. Five decision analysis criteria are analyzed for finding the best location for a new facility under these circumstances. We first design a general approach that can be applied to many location problems. We then prove that by the optimistic rule, the solution is one of the extreme cases and construct solution algorithms to solve problems based on the other four rules optimally. The approach is demonstrated on the MP trips model. We performed computational experiments on instances between 100 and 20,000 demand points on five different models and two distance decay functions. The largest problem was solved within a given accuracy in about 8 h for the most time-consuming model. Our general approach can be applied to other location models (not necessarily competitive) when a parameter of such models is stochastic.

Applied Techniques for Twitter Data Retrieval in an Urban Area: Insight for Trip Production Modeling

This paper presents methods of retrieving Twitter data, both streaming and archive data, using Application Programming Interfaces. Twitter data are a kind of Location Based Social Network Data that, nowadays, is emerging in transportation demand modeling. Data regarding the locations of trip makers represent the most crucial step in the modeling. No research article has specifically addressed this topic with an up-to-date method; hence, this paper aims to refresh methods for retrieving Twitter data that can capture relevant data. The method is unique as the data are gathered for trip production modeling in zonal urban areas. Python script programs were built for both data retrieving methods. The programs were run for streaming data from May 2020 to April 2021 and archive data from 2018. The data were collected within Serang City, which is the nearest provincial city to Jakarta, the capital of Indonesia. In order to gather streaming data with no loss, the program has been run with referencing on sub-district office coordinate locations. Retrieving the intended data produces 1,090,623 documents, of which 54,103 are geotagged data from 2495 users. The study concluded that streaming data produce more geolocation data, while historical data capture more Twitter user data with relatively very little geotagged data and greater textual data than the period covered in this research. Thus, both techniques of retrieving Twitter data for urban personal trip modeling are necessary. Obtaining sufficient data collection using data streaming retrieval resulted in the most effective data preprocessing. This research contributes to Location Based Social Network data mining knowledge, both geolocation and text mining, and is useful for insight into developing trip production modeling in passenger transportation demand modeling using Machine Learning. This study also aims to provide useful methods for transportation system researchers and data scientists in utilizing Location Based Social Network data.

Adversarial Human Trajectory Learning for Trip Recommendation.

The problem of trip recommendation has been extensively studied in recent years, by both researchers and practitioners. However, one of its key aspects-understanding human mobility-remains under-explored. Many of the proposed methods for trip modeling rely on empirical analysis of attributes associated with historical points-of-interest (POIs) and routes generated by tourists while attempting to also intertwine personal preferences-such as contextual topics, geospatial, and temporal aspects. However, the implicit transitional preferences and semantic sequential relationships among various POIs, along with the constraints implied by the starting point and destination of a particular trip, have not been fully exploited. Inspired by the recent advances in generative neural networks, in this work we propose DeepTrip-an end-to-end method for better understanding of the underlying human mobility and improved modeling of the POIs' transitional distribution in human moving patterns. DeepTrip consists of: a trip encoder (TE) to embed the contextual route into a latent variable with a recurrent neural network (RNN); and a trip decoder to reconstruct this route conditioned on an optimized latent space. Simultaneously, we define an Adversarial Net composed of a generator and critic, which generates a representation for a given query and uses a critic to distinguish the trip representation generated from TE and query representation obtained from Adversarial Net. DeepTrip enables regularizing the latent space and generalizing users' complex check-in preferences. We demonstrate, both theoretically and empirically, the effectiveness and efficiency of the proposed model, and the experimental evaluations show that DeepTrip outperforms the state-of-the-art baselines on various evaluation metrics.

Parental perspectives on the quality of life of children with cochlear implants in Armenia

Abstract Objectives The aim of this project is to assess the quality of life (QOL) of children living with cochlear implants (CIs) in Armenia using the ‘Children with Cochlear Implants: Parental Perspectives’ (CCIPP) questionnaire. Design A group of children received CIs after an international medical mission trip. Parents of 39 children who received CIs (30 male and 9 female, age range between 1 and 7 years, mean 6.31 years post-implantation, and mean age at time of implantation 2.92 years old) completed the CCIPP questionnaire. The questionnaire had 107 items, which were scored using a 5-point Likert scale. Results General functioning, communication, and support of the child domains (mean 4.13, 4.01, and 4.00, respectively) achieved the highest ratings. Effects of implantation (mean 2.97) and education (mean 3.24) received less positive ratings. Communication, general functioning, social relationship, and well-being and happiness had the most positive correlations with other domains (P < 0.05). Significant but weak correlations were observed between communication and age of implantation (rs = −0.352, P < 0.05), general functioning and number of spoken languages (rs = 0.372, P < 0.05), and supporting the child and number of spoken languages (rs = 0.376, P < 0.05). Conclusion The parents positively rated most domains indicating that the QOL of the child at ≥3 years post-implantation is improved and that parents view the implant as having a positive impact on their child’s life. Future studies should investigate and implement solutions to enhance parents’ satisfaction with education and effects of implantation domains by improving resource allocation to schools and providing better preoperative counseling to parents, respectively. Importantly, this project demonstrates that an international medical aid trip model can be an effective means of providing cochlear implantation to resource-limited countries.

Smart Trip Prediction Model for Metro Traffic Control Using Data Mining Techniques

In the urban rail transportation industry, collecting various data, including the number of trips, the number of fleet breakdowns, the number of breakdowns of railway lines, etc., contributes significantly to future planning and optimization of resources and costs. We are faced with big data analysis due to the daily production of large volumes of data in the urban rail transport industry. This study aims to find a trip prediction model for Tehran Metro to do trip decision-making. This paper provides a brief introduction to knowledge discovery from failure databases and presents the data mining methodology. The relevancy of data mining for a trip prediction model will be depicted. In this sense, considering the relevance of data collected on the metro control center and intelligent trip prediction, this paper presents a functional architecture of a predictive trip model using data mining techniques. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in the daily operation. This research has investigated three situations, including delayed trips, incomplete trips, and canceled trips, using actual data recorded over the past four years, including non-programmed failures and their impact on trips. A model with more than 90% (90.48%) accuracy suggests a trip status forecast on this line. Also, the research has used Rapid Miner software and the Decision tree technique in data mining and its rules. Also, the kappa index of 0.792 is calculated for the validation model. Therefore, the proposed model enables the prediction of three types of incomplete traffic, delayed trips, and cancellation of trips due to emergency failure at the time of the operation of the Tehran Metro.

How do the socio-economic indicators influence the travel behaviour pattern?: Case study Amman-Jordan

Limited research has been conducted in developing countries on travel behavior and its prediction. This study aimed to investigate if socio-economic conditions affect travel behavior patterns in Amman, Jordan, and propose regression models for trips defining the contributory factors. A total of 681 interviews were conducted with households (210) and workplace employees (335), assessing behavioral travel styles in two neighborhoods with different characteristics. Compared to residents of high-income areas (HIA), residents of low-income areas (LIAs) travel more by all modes of transportation; in LIA and HIA, the trip rate per person was (2.2) and (2.0), respectively, while in low-income and high-income areas, the number of trips per household was 5.14 (153.6 minutes) and 3.7 (155 minutes). Most household trips in low-income neighborhoods, mainly for education and work, were made on foot, while private cars were more common in high-income areas. For trips related to the office and shops, the private car was the most common mode of transportation. In low-income neighborhoods, shared taxis were commonly used for household and shop trips, and buses were often used for commutes. School and university students, as well as household size, provided valid trip predictions. Employees can predict work trips to the office, customer visits, and shop-related trips.

Predicting Mobility as a Service (MaaS) use for different trip categories: An artificial neural network analysis

• This study produces predictive models for MaaS use by applying artificial neural network (ANN) analysis. • MaaS use models are developed separately for social, general, and work trips. • The cross industry standard process and data mining framework is followed in designing ANN-based predictive models. • Different sets and combinations of socio-economic, personal travel and Covid-19 related factors are key predictors of MaaS use for social, general, and work trips. • Findings can inform strategies and policies to promote MaaS use. Mobility as a Service (MaaS) has gained popularity as a means of sustainable urban transport which makes the understanding of MaaS use critical for its design and promotion. Studies have contributed to the growing understanding of MaaS, its design features, and consumers’ willingness to pay for MaaS in general, without recourse to the differences in traveling for work, social and general purposes. This paper develops and tests models to predict MaaS use by applying artificial neural network analysis, following the cross industry standard process and data mining framework. It also estimates separate models for social, general, and work trips, using 33 input variables reflecting network externality, transaction cost, behaviour, institutional, environmental concern, personal travel, and socio-economic factors. Data are collected from a survey of 331 Australians. The study reveals different sets of socio-economic factors, the impact of Covid-19, and personal travel factors as key predictors of MaaS use for general, social, and work trips with an average prediction accuracy of 68%, 68%, and 75% respectively. The findings can be used to inform strategies and policies on how to attract a user base with respect to socio-economic and personal travel factors for promoting MaaS use.

The Round Trip Effect: Endogenous Transport Costs and International Trade

Container ships travel between a fixed set of origins and destinations in round trips, inducing a negative correlation in their freight rates. I study the implications of this round trip effect on international trade and trade policy. I identify this effect and develop an instrument using it to estimate the impact of transport costs on trade. I simulate counterfactual import tariff increases in a quantitative model and quantify the importance of endogenizing transport costs with respect to this effect: an exogenous transport costs model predicts a trade balance improvement from protectionist policies, while the round trip model finds the opposite. (JEL D22, F13, F14, L92, R41)

IMPACT ANALYSIS OF URBAN FREIGHT TRIPS GENERATED FROM WHOLESALE MARKET, AHMEDABAD

Freight transport plays an important role in meeting the domestic needs of the city's inhabitants. But when freight vehicles come into contact with city traffic, it has a negative impact on urban routes, such as extra traffic congestion, noise pollution, air pollution, etc. The aim of the study is to assess the impact of freight transport on the overall traffic of the city. A case study of the wholesale market of Ahmedabad has been considered as a study area. The freight trip models based on the trips attracted and produced by the market are developed on the basis of data collected from establishment surveys and freight vehicle driver surveys. Both the models developed in this study, FTA (Freight Trip Attraction) and FTP (Freight Trip Production), have R Square values of 0.799 and 0.715, respectively. The volume of freight vehicles contributing to the overall traffic flow is also measured by the traffic volume study. Through the data analysis and identification of the impact of freight transportation on city traffic, remedial measures are discussed to reduce the impact of freight transportation.

Increasing students’ culture literacy using virtual reality field trip model: Need analysis

The rapid exchange of information through technology has caused a cultural change in Indonesia. This causes the local culture to be lost and the penetration of foreign cultures. This article aimed to analyze the need to develop Field Trip Model Virtual Reality for elementary school students. This study used the qualitative descriptive method. The 50 students and 28 teachers from an elementary school in Jakarta became participants. The data were collected using questionnaires form and interviews. The data were analyzed using triangulation. The result showed that the learning model was needed to increase cultural literacy. From questionnaire data show that (1) students need learning media that can make it easier to recognize the culture, (2) students' awareness of culture is still low, and (3) students need technology that allows users to feel the conditions as real from the three-dimensional effects. 
 
 Keywords: media; VR, cultural, literacy, school

Estimating mode choice of motorized two-wheeler commuters under the influence of combined travel demand management measures: An ICLV modeling approach

Several studies have reported the need for combining multiple Travel Demand Management (TDM) measures for minimizing the use of private vehicles. However, a detailed empirical analysis is needed to substantiate the theory and understand the factors influencing the resultant mode choice behavior. This paper presents a stated choice experiment study to examine the mode choice behavior under the influence of a combined TDM package of Congestion Pricing (CP) and Public Bike Sharing (PBS). The case study area is the old city of Ahmedabad, which is in the western state of Gujarat, India.A stated choice experiment was conducted, wherein a total of 1,719 data points randomly collected from 573 motorized two-wheeler commuters were used to simultaneously estimate an Integrated Choice and Latent Variable (ICLV) model for work trips in the study area. In addition to the observable variables associated with an individual's socioeconomic and trip characteristics, the model also includes the psychological theory of Value-Attitude-Behavior (VAB) in the choice modeling framework. The results show that there exists a statistically significant influence of values of benevolence and stimulation as well as attitudes towards the flexibility of mode, environment, and health in explaining individual preferences. Furthermore, the modeling results also suggest that the combined TDM measures are likely to significantly influence mode choice decisions as opposed to standalone TDM measures.