Trip Generation Research Articles

The Forecasting Model of the Impact of Shopping Centres in Urban Areas on the Generation of Traffic Demand

The increase in traffic caused by new development affects the change in traffic conditions on the surrounding roads, and shopping centres are significant traffic generators. The development of local travel generation rates and their characteristics for individual land uses from the aspect of traffic demand is a reliable way to plan traffic in order to come up with preventive solutions to traffic problems, that is, prevention of possible negative consequences on traffic conditions in the street network occurring due to the construction of shopping centres. One of the main aims of this paper is to develop a model for objective assessment of the generated traffic demand for significant changes in land use, such as the construction of shopping centres in medium-sized towns. All these would be steps in the right direction for the promotion of reliable traffic planning and adoption of TIA for every new development before a decision regarding the change in land purpose has been made. This kind of process still has not been established systematically in either Bosnia and Herzegovina and the Republic of Serbia, or in surrounding countries. This paper focuses on the formulation of a model for determining the volume of traffic generated by shopping centres in medium-sized towns in two countries of the Southeast Europe region. The survey was conducted in eight different locations (cities) where there are shopping centres with common facilities. The analysis showed that the number of visitors and vehicles attracted by the shopping centre zone can be determined by a model based on a linear regression analysis. The analysis included exploring several different factors of trip generation in shopping centres, including the relationship between trip generation and combinations of several independent variables. The verification of the model was conducted in real conditions of the traffic flow generated by a shopping centre which was not the analysis subject when forming the forecasting model. In this way, the validity of the proposed model is credibly assessed. The developed model can be applied in the procedures of planning the construction of shopping centres in medium-sized cities in the Republic of Serbia and Bosnia and Herzegovina, and wider, in the region of Southeast Europe, in order to estimate the volume of generated traffic demand, that is, its impact on the conditions of traffic on the surrounding traffic network.

Revealing the impacts of COVID-19 pandemic on intercity truck transport: New insights from big data analytics

Intercity truck transport emerged as a crucial lifeline for maintaining city operations during COVID-19 pandemic. Understanding pandemic-imposed impacts on intercity truck transport can inform policymakers in crafting more effective strategies for future crises and disruptions. However, to our best knowledge, previous research predominantly focused on freight movements under normal circumstances. Due to the data limitation, the pandemic-related studies commonly relied on freight survey and focused on specific industries, which cannot capture the full spectrum of factors influencing freight trip generation (FTG) during the pandemic. Here, a novel dataset capturing large-scale individual truck movements during the COVID-19 pandemic is provided. By leveraging the mobility dataset, pandemic-induced changes in truck transport demand structure are quantified using spatial statistical methods. Furthermore, an interpretable machine learning framework for intercity freight demand estimation is developed, revealing the complex interplay of factors that influence and shape the behavior shifts of intercity truck transport systems due to the pandemic outbreak. The findings suggest significant changes in various factors influencing intercity truck movements across local and broader regions, emphasizing city-specific challenges amidst pandemic. The developed FTG model could serve as a tool to predict freight demand between cities for future crises and to support policymaking in the practice of freight management.

Understanding human mobility and trip demand through sparse trajectories of private e-bikes

Understanding human mobility and trip demand through e-bike trajectories is crucial for urban planning, environmental enhancement, and sustainable development. However, existing studies predominantly focus on shared (e−)bike trips, neglecting private e-bike trips. With the recent availability of sparse trajectory for private e-bikes, we established a novel analysis framework to reveal human mobility and trip demand in Wuhan, China. First, we propose a two-step method for extracting trip behavior from sparse trajectories of private e-bikes, involving the identification of staying areas and the generation of e-bike trips. Second, we establish a spatial random forest method to capture the nonlinear relationship between private e-bike trips and driving factors. Finally, we use the interpretable SHAP method to reveal the driving mechanisms of e-bike trips and explore the impact of various factors on these trips. The results indicate that (1) trip distances of private e-bikes follow a lognormal distribution, with an Adj. R-Square of 0.99, while trip times exhibit a Hill distribution, with an Adj. R-Square of 0.95; (2) Private e-bike trips are not commonly employed to address the first/last mile problem in public transportation and are more frequently used for daily commuting needs, with over 65% of these trips covering distances greater than 1 km or lasting longer than 5 min; (3) private e-bike trips positively correlate with the density of POIs like Hospital, School, and Transportation Station. However, compared to shared (e−)bike trips, Transportation Station Density, especially Metro Station Density, is less important for private e-bike trips; and (4) private e-bike trips are also positively correlated with Congestion Level and House Price, meaning that areas with severe traffic congestion or high housing prices tend to have more private e-bike trips. This study provides a new framework for understanding private e-bike trip patterns, also helping authorities better grasp the factors influencing e-bike trip demand.

Analisa Bangkitan Perjalanan di Wilayah Komplek Perumahan Safira Kota Jambi

From data from BPS (Central Statistics Agency) Jambi City, the population of Jambi City in 2022 is 619,553 people with an area of 205.38 km2, has 11 sub-districts. Safira Housing is one of the housing complexes located in Talang Bakung Village, Paal Merah District, Jambi City. Population Safira housing ± 455 people. The increase in trips made by a number of Safira housing communities will cause traffic jams during peak hours such as when leaving and returning from work. This research method uses multiple linear regression with independent variables consisting of the number of family members X1, the number of family members who work X2, number of family members attending school x3, with trip generation as variable Y and then analyzed using the SPSS application. The variable that has a dominant influence on the dependent (dependent) variable is based on the standardized coefficient beta value, namely the variable number of family members attending school/college (X3), number of family members (X1), and number of working family members (X2). The trip generation analysis model at Safira Housing is: Y 2.257 + 0.275 X1 + 0.227 X2 + 0.528 From this model, the factors that influence the generation of Safira Housing trips are obtained, namely the number of family members (X1), the number of family members who work (X2), the number of family members who go to school/college (X3). Based on the R Square value of 0.621, this means that 62.1% of trip generation at Safira Housing is explained by the independent variables in the form of number of family members, number of family members working, number of family members attending school/university.

Kombinasi Model Trip Generation-Modal Split (G-MS) Dalam Perencanaan Bus Bandara Terintegrasi

The modal split model can be varied to other model in the concept of four step models transportation. This approach model can be varied depending on the purpose of transport planning, such as the modal split planning process which is carried out at the trip generation calculation stage, and in this process the movement of public transport can be directly separated from private transport with the assumption that someone who will travel can choose the model that is influenced by socioeconomic valiable. The model combined experiment (G-MS) was carried out on the route to and from Jambi’s Sultan Thaha Airport, which is one of the transportation nodes that plays an important role in regional development in Jambi City. The aim of this research is to analyze potential demand using a combination of the Trip Generation-Modal Split (G-MS) model. Based on the results of the analysis using the binomial ratio logit model with the value Y = Proportion of passengers/days. The passenger distribution area is devided base on zones where the zones that have the potential to be passenger gathering point are zone 9, zone 10 and zone 11. Calculation of the chi-square test (goodness of fit test) shows that the calculation X2 value is 11.583 and the table X2 is 14.067 because X2 count <X2 table, then H0 is accepted which means the observation results are the same as the average model results so that model validation is declared successful, so this research can be used to calculate passenger demand to plan airport buses.

An Estimation Method for Passenger Flow Volumes from and to Bus Stops Based on Land Use Elements: An Experimental Study

To unravel the general relationship between bus travel and land use around bus stops and along bus routes and to promote their coordinated development, this paper explores a method to estimate passenger flow volumes from and to bus stops based on land use types, intensities, and spatial distributions around bus stops and along bus routes. Firstly, following the principle of the gravity model, which considers traffic volumes analogous to gravity based on trip generation and distance impedance between traffic analysis zones (TAZs), a gravitational logic estimation method for passenger flow volumes from and to bus stops was constructed with land use elements between bus stop TAZs and the upstream and downstream collections of bus stop TAZs. Building upon this, the passenger flow volumes from and to 38 bus stops in the Xueyuan Square area of Dalian during weekday morning peak hours were taken as the experimental objects. The basic estimation models of two gravity sets corresponding to passenger flow volumes from and to bus stops were constructed using the bus travel generation based on the aggregation of area-based origin unit method and the bus travel distance impedance based on the probability density method. Finally, the reliability of the estimation method of passenger flow volumes from and to bus stops was verified by regression fitting between the surveyed values of passenger flow volume and the estimated values of the basic models. The results indicate that the fuzzy estimation and transformation of bus travel based on land use elements, which serves as a crucial lever for facilitating strategic alignment in transit-oriented development (TOD), can be effectively achieved by using the area-based origin unit method to aggregate bus travel generation and the probability density method to evaluate the bus travel distance impedance.

A Development of Trip Generation Model by Functions of Small City Based on Mobile Origin-destination Data

A Development of Trip Generation Model by Functions of Small City Based on Mobile Origin-destination Data

MASS RAPID TRANSIT (MRT) DEMAND AND CAPACITY ASSESSMENT FOR RAIL TRANSPORT IN MALAYSIA

Assessing the quality of MRT services unveils a generally satisfactory experience among respondents, with comfort emerging as a particularly noteworthy aspect. Satisfaction levels for MRT service quality range from 3.63 to 4.12, indicating a favorable perception overall. Notably, comfort stands out with the highest satisfaction rating, surpassing the "Satisfied" threshold, while accessibility receives the lowest rating. In essence, the majority of MRT passengers’ express contentment with the service, spanning operation, maintenance, and performance. Additionally, Bukit Bintang station emerges as a focal point during peak hours due to its proximity to diverse amenities. However, the prevalence of respondents underscores the necessity for broader demographic representation in future studies. Anticipated increases in MRT ridership necessitate proactive measures to accommodate the growing demand, with projected passenger numbers expected to rise significantly. Peak demand for the MRT SBK line is estimated at 115,498.543 passengers per day, reflecting a notable escalation of 281.82% across various purposes. Yet, reliance on responses may introduce biases in trip generation models and subsequent travel demand forecasting, emphasizing the importance of diversifying the respondent pool for more accurate predictions. Overall, the evaluation underscores the satisfactory nature of MRT services, particularly in terms of passenger comfort.

Predicting cycling flows in cities without cycling data

Cycling is a potential tool to mitigate many of the problems faced by urban populations today. Encouraging the use of bicycles as a legitimate mobility tool, however, demands adequate knowledge of current mobility patterns, such as locations of trip generation and attraction. Unfortunately, cities usually do not gather enough data to adequately understand cycling demand. We propose models based on spatial econometrics and gradient boosted regression trees which can be trained with data from cities with mature cycling cultures and then applied to cities still in their cycling infancy to supply city officials with a better estimate of potential future OD matrices. We perform a case study in the Boston Metropolitan Area and show results comparing both types of models.

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.

The movement pattern of app-based transportation in Banda Aceh City, Province of Aceh, Indonesia

Abstract Transportation plays a crucial role in fulfilling people’s need to conduct their activities. Cities established transportation systems to serve the needs of people to travel. Technological advancements have led to the emergence of app-based transportation. App-based transportation has emerged as an alternative to public transportation and personal vehicles. The advantages of app-based transportation, such as flexibility, 24-hour services, swift mobility, and convenience, have influenced citizens’ travel behavior. App-based transportation has been available in Banda Aceh since 2017. This research aims to study the movement pattern of app-based transportation in Banda Aceh. The data on the origin and destination of app-based transportation users were collected from 100 drivers using the accidental sampling method. This research utilized GIS software to analyze the movement pattern. The study shows that based on land use, the app-based transportation origin and destination occurred mostly in settlement areas. The highest trip generation occurred in the education center area, followed by the main city center. A polycentric pattern, with the education center as the main origin and destination point, characterizes the movement patterns of app-based transportation in Banda Aceh. It indicates that the main users of app-based transportation are students living around the education center.

Development of Trip Generation Model Using Linear Regression for Areas in Hyderabad City

Development of Trip Generation Model Using Linear Regression for Areas in Hyderabad City

Robust digital-twin airspace discretization and trajectory optimization for autonomous unmanned aerial vehicles

The infiltration of heterogenous fleets of autonomous Unmanned Aerial Vehicles (UAVs) in smart cities is leading to the consumerization of city air space which includes infrastructure creation of roads, traffic design, capacity estimation, and trajectory optimization. This study proposes a novel autonomous Advanced Aerial Mobility (AAM) logistical system for high density city centers. First, we propose a real-time 3D geospatial mining framework for LiDAR data to create a dynamically updated digital twin model. This enables the identification of viable airspace volumes in densely populated 3D environments based on the airspace policy/regulations. Second, we propose a robust city airspace dynamic 4D discretization method (Skyroutes) for autonomous UAVs to incorporate the underlying real-time constraints coupled with externalities, legal, and optimal UAV operation based on kinematics. An hourly trip generation model was applied to create 1138 trips in two scenarios comparing the cartesian discretization to our proposed algorithm. The results show that the AAM enables a precise airspace capacity/cost estimation, due to its detailed 3D generation capabilities. The AAM increased the airspace capacity by up to 10%, the generated UAV trajectories are 50% more energy efficient, and significantly safer.

Measuring Covid-19 effect on rail passenger flow with region-based trip generation models

The Covid-19 pandemic affected the lives of all humans in their daily routines, and the transportation sector became one of the most affected services worldwide. Different instruments are used to enable the effect of the pandemic on transportation to be measured. In this study, trip generation modelling is performed first to understand what factors trigger trip making and how they affect rail passenger flow (RPF) in general; the aim is to measure the loss in RPF due to the Covid-19 pandemic using these trip generation models. Daily RPF data from station to station in Türkiye during the years 2011–2019 are used for the trip generation models and 2016 rail passenger survey data are used for understanding the geographical characteristics of the flow. It is seen that instead of one model covering the whole country, it is more suitable to form different models for specific regions. Using region-based trip generation models, the Covid-19 effect on RPF on the mainlines was measured for the year 2020. Modelled values are anticipated numbers for the no-pandemic condition; therefore, by comparing the modelled and observed data, it is possible to understand how Covid-19 affected the traffic.

Change in work arrangement during the COVID-19 pandemic: A large shift to remote and hybrid work

With two repeated cross-sectional datasets collected in Southern California, we investigate changes in work arrangement and commute trip generation across four timepoints before and during the COVID-19 pandemic: fall 2019 (recalled in fall 2020 retrospectively); fall 2020 and summer 2021 (reported at the moment); and summer 2022 (an expectation as of summer 2021 for a near future prospectively). Our results highlight a large shift from physical commutes to exclusively/predominantly remote work for many workers in fall 2020, followed by a transition towards hybrid work (combining in-person work and remote work) in summer 2021. The adoption of remote/hybrid work varied considerably across different types of workers and sociodemographic groups, prominently driven by high-income earners, highly educated individuals, urban residents, full-time workers, and those with white-collar, STEM (science, technology, engineering, and mathematics), and government jobs. As the pandemic subsides, many workers have returned to physical commutes for some of their workdays, but the prevalence of remote/hybrid work is expected to endure into the future. These findings confirm oft-discussed trends and anecdotal evidence in the study region. However, the study provides rigorous quantitative evidence into reasons behind these observations and heterogeneity across groups. We also suggest forward-looking policies to promote transportation equity, reimagine transportation options, and support public transit to adapt to the evolving travel patterns in the post-pandemic society.

Powering bikeshare in New York City: does the usage of e-bikes differ from regular bikes?

ABSTRACT In this study, we investigate the difference between shared electric bicycles (e-bikes) and conventional shared bikes operated by Citi Bike in New York City. We examine differences in usage by examining summary statistics and we develop conditional autoregressive models to examine differences in factors associated with trip generation. These factors include bike infrastructure, subway proximity, area demographics, land use, and elevation. We also control for border effects by identifying peripheral stations. The percentage of e-bike trips fluctuates across hours of the day, with a peak around 3 PM but diminishes during peak commuting hours. We find mostly no significant differences in factors associated with the generation of trips between the two modes. Exceptions include a larger effect on e-bike trip generation when the daytime worker population is larger; other differences are due to the racial composition around the station area, some land use categories, and elevation differentials.

A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY

Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.

Developing Trip Generation and Attraction Models Using High-Frequency Proxy Data

The attraction and generation of a trip play a crucial role in transport planning and forecasting of trips. Multiple linear regression (MLR) is the most popular method of calculating trip attractions (TA) and trip generations (TG) to produce a distribution that can be used to forecast trips with updated values of independent variables such as electricity consumption, no of households, area of the land use etc. Literature shows that the surveys require independent variables to be updated; making them expensive and time consuming. This study aims to develop an MLR model for TA and TG based on available survey data from 2013 for Western Province, Sri Lanka and to update those independent variables using High Frequency (HF) proxy data for the predicted year (2019). Data from HF proxy sources such as electricity consumption, GPS customer points, and Landsat satellite imagery data have been used to update the independent variables of TG and TA for 2019. In the initial stage of this research, data from a home visit survey conducted in 2013 and land use data from the Western Province of Sri Lanka were used to develop the MLR model for TG and TA. A correlation and a regression analysis were performed using these surveyed data. According to the study, the MLR model for TG for home-based work trips has an r2 value of 0.79 and TA for general purposes (including shops and businesses) and industrial has an r2 value of 0.74 and 0.79, respectively, indicates the strong relationship between the considered variables to predict TG and TA. The model is validated with 2013 survey data and would be helpful for real-time estimation of the TG and TA of each zone.

Evaluating spatial inequity in last-mile delivery: a national analysis

PurposeDespite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.Design/methodology/approachThe model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).FindingsEvaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.Originality/valuePresents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Analisis HBU dan Peluang Kejenuhan Lalu Lintas pada Pengembangan Plasa Simpang Lima Semarang

Simpang Lima Plaza is a Semarang City Government asset which is located in the city center, precisely on Jalan Ahmad Yani, Semarang City. The Plaza Simpang Lima building was built in 1990 with a building area of 38,935 m2. Plaza Simpang Lima is managed by PT Argamukti with a 30 year contract system which will end in 2024. When the contract ends, the age of the Plaza Simpang Lima building will be 30 years, so the use of the Plaza Simpang Lima building needs to be reviewed in relation to its potential utilization. Previous research discussed asset optimization but ignored the impact on traffic potential of selected land uses. This research was conducted to find out the most effective land optimization and the resulting traffic impacts. In this research the analysis was carried out using the Highest and Best Use method to determine alternative uses that are appropriate and technically and financially possible to determine the optimum value and the trip generation method to determine the potential traffic impacts that occur from the selected land use. The results of this research were obtained that the alternative effective use of land is a mall which will result in an increase in land value in the 7 year projection period of IDR 379,259,450,817 with an increase in roundabout delays of 35.23% and the chance of this condition occurring is 36.2%.