Trip Attraction Research Articles

Trip Generation for shopping malls in Developing Cities

This study develops trip generation models for shopping malls in Mumbai, one of the fast-developing cities. A total of eighteen shopping malls are selected to develop trip generation models using regression analysis. The factors that found significant in weekdays and weekend trip attraction models include built-up area, number of screen and seats in multiplex, number of kiosk and stores, number of parking spaces and gross floor area. Based on the video data collected at two shopping malls, it was found that the peak period is from 6 PM to 9 PM. The findings of this study can be used analyze the impacts of newly constructed shopping malls and plan transportation infrastructure in and around a shopping mall.

Urban freight characteristics and externalities – A comparative study of Gothenburg (Sweden) and Delhi (India)

Background: The urban freight transportation (UFT) system is an essential facilitator for the smooth functioning of city logistics. It also contributes significantly to transportation-related issues, such as vehicular congestion, pollution, accidents, infrastructure damage and other related problems.Objectives: This study aimed to understand and compare the urban freight traffic characteristics and their impact in two cities: Gothenburg, Sweden and Delhi, India, having high socio-economic differences. The findings of this study will be helpful for practitioners and policymakers in the development and successful implementation of UFT management policies.Method: Using the cordon count (traffic data), retailers’ interviews (receivers’ data) and freight vehicle drivers’ interviews, UFT characteristics such as sector-specific contribution, trip characteristics, vehicular characteristics and negative impacts in terms of external cost to society are evaluated. This study also discusses the prediction of attracted trips in the study zone using freight trip attraction (models.Results: About 75% of the trips in Gothenburg and 92% of the trips in Delhi are made by light commercial and other small-sized vehicles. Gothenburg has formally organised sectors involved in freight vehicle movement; however, Delhi has informally organised sectors.Conclusion: The results revealed substantial differences in characteristics of freight trip operations in both the cities, compared with few similarities. Measures taken considering the specific local attributes of freight traffic and stakeholders’ requirement may lead to a sustainable transportation system in cities.

Model Tarikan Perjalanan Pengunjung Pusat Perbelanjaan Java Mall Semarang

Java Mall is a shopping center in the city of Semarang which is located on a secondary collector road, namely the MT. Haryono which is the main trade route of the city of Semarang and is included in one of the Petawangi areas (Peterongan - Tawang - Siliwangi), namely the Peterongan trading area. The development of the development of the Java mall is a special attraction for visitors because every month there are events in the form of computer exhibitions, property, cars, etc., in addition there are cinemas and food markets. The problem that occurred in this research location is the traffic jams that occur due to the influence of the pull of the Java Mall shopping center directly accessed on Jl. MT. Haryono (Semarang). Congestion occurs on the road in front of the Java Mall due to the pull of the Java Mall so that new activities arise such as the use of road bodies for parking vehicles, online motorcycle taxi stops on the shoulder of the road, and street vendors who use the shoulder of the road. Based on these problems, preliminary research was conducted on travel attraction modeling to understand the contribution of Java Mall visitor vehicles to Mt. Haryono based on the development of the area of Java Mall activities. In this study quantitative methods are used. Trip attraction modeling in multi-activity shopping centers is influenced by the floor area of each building shown by the equation Y = 0.003 + 0.151 X1 + 0.052 X2 + 0.066 X3 + 0.091 X4. Calculation of the total trip rate of visitors resulting in a trip attraction generated by the Java Mall during peak hours is 0.066 people / m2, in this case the development of the Java Mall building is considered quite effective with several areas of activity therein.

Receiver willingness to participate in off-hour service programs

Service trips, a frequently overlooked segment of urban traffic, represent a disproportionately large share of the negative impacts associated with commercial activity. The reason is that although service trips are less frequent than freight trips, they often take longer and thus occupy a significant share of commercial parking. Demand management programs seeking to move service trips off-hours can potentially reduce congestion and emissions but they have been understudied. This paper describes research conducted to investigate receivers’ willingness to participate in off-hour programs for planned service activity. The research reported in this paper analyzes results from a survey of 189 business establishments in New York City and the Capital Region in New York State using descriptive analyses of the data, and by estimating a discrete choice model to gain insight into how business characteristics influence willingness to participate in off-hour services. To assess the impacts of a hypothetical off-hour services program, the authors applied the discrete choice model, together with service trip attraction models, to quantify the hours of daytime parking that could be eliminated by the proposed program in certain ZIP Codes in New York City and the Albany area. The research conducted led to the identification of numerous policy implications that will help policymakers understand and maximize the potential benefits of implementing off-hour service programs.

Property value capture for development of mass rapid transit: Case study from Hanoi

Property value capture is expected to financially support investments in mass rapid transit (MRT) in many developing countries where conventional public funding is insufficient to meet the local demand. This study presents a case study to examine the financial feasibility of developing and operating a new metro line through property value capture and land-use control in Hanoi. First, property prices are estimated with local data for both residential and office properties, using hedonic price models. Next, a travel demand model, including trip generation and attraction, trip distribution, and travel mode choice sub-models, is estimated with person trip survey data, following a conventional four-step model framework. Subsequently, multiple scenarios are built for a hypothetical investment in an MRT line in Hanoi. The scenarios are assumed considering socio-demographics and transportation infrastructure in the context of new taxation levied on local properties near MRT stations and land-use controls with respect to population and employment to increase their densities near MRT stations. The estimated models are applied to each scenario to estimate the expected financial viability of MRT development and operation. The results reveal that the property value capture and land-use control could significantly improve the operator's profitability.

MODEL ESTIMASI BANGKITAN PERGERAKAN MODA LAUT MENGGUNAKAN METODE REGRESI LINIER DAN RANDOM FOREST

The need for people and goods movement across regions have been increasing susbtantially over the years. Sea transportations plays pivotal role in allowing this trans-national movement.The ferry line connecting Langsa, Indonesia and Penang, Malaysia had been established to help people and good across borders, albeit no longer operating. This study aims to estimate trip generation and attraction between these ports and their surrounding areas, and suggest whether this ferry line should reoperate. In doing so, random forest was utilised alongside linear regression model. Random forest has been increasingly popular among researchers in making estimates or preditions, whether calculating numerical or categorical data. To develop both models, historical trip and socio-economics data were employed. It was found that Gross Domestic Regional Product (GDRP) and population significantly affect trip generation and attraction. Both variables shows positive effect on trip generation and attraction: an increase in either should increase trip generation and attraction. Similar to linear regression model, random forest model show an excellent predictive capability. With a sophisticated model incorporating more variables, it is expected that random forest should well exceed linear regression predicitve capability. As population and GDRP are expected to continue to grow, it is suggested that stakeholders should reoperate this Langsa-Penang ferry line.

DETERMINATION OF ATTRACTION BASED ON THE LAYER OF THE REGION ROAD MR. TEUKU MOH. HASAN

The spatial structure of Banda Aceh City with the strategy of developing Banda Aceh RTRW 2009-2029 describes city activities in a schematic manner, namely divided into old city centers and new city centers. The new city center is centered in the Batoh or Lamdom area which administratively will serve Lueng Bata and Baiturrahman sub-districts.The formulation of the problem is how the relationship between land use and trip generation on Teuku Moh. Hasan Street East side Hasan. In order to know the capacity and land use influence which is caused to the road, it is necessary to know the capacity, volume, and factors causing the attraction and generation at the location. The results of data collection and processing obtained from the capacity Teuku Moh. Hasan Street is 2696.1 smp/hour. The generation model obtained in layer 1 of the research location which is a commercial land use is Y = -0.629 + 0.056 X2 + 1.002 X3, for layer 2 the research location which is a residential land use is Y = 0.409 + 1.179 X1 + 0.588 X3 + 0.440 X5 and layer 3 research locations which are residential land use are Y = -1.057 + 1.796 X1 + 0.408 X2.Keywords: trip generation and attraction, land use, regression method

Spatial Seemingly Unrelated Regression Models for Freight Trip Generation by Vehicle Type: Application to the Chennai Metropolitan Area in India

During the last two decades, there has been substantial interest in developing freight trip generation (FTG) models. Most studies consider only truck trips or convert all freight trips into equivalent truck trips. Freight in several large cities is increasingly being moved by smaller vehicles. This calls for modeling FTG by vehicle type. The present research identifies and compares establishment characteristics affecting FTG by different vehicle types. In this context, spatial correlations among nearby establishments and the error-term correlations between independent models by vehicle type become relevant. Based on the Lagrange-Multiplier (LM) tests, we develop non-spatial seemingly unrelated regression (SUR) models for freight trip production (FTP) and spatial SUR models with a spatial lag in the dependent variable to account for both spatial and error-term correlations for freight trip attraction (FTA). The results show that establishment type and size affect FTG by different vehicle types.

Hospital Trip Production and Attraction Modeling for Future Predictions

The demand for healthcare facilities in the world is growing daily, and this growth in demand exceeds general population growth. This situation has made hospital trips a vital issue during urban transportation planning. In this context, the aim of this study is twofold: (1) to investigate the hospital trip behavior as home-based hospital trips within the scope of trip production and attraction (trip generation) models and (2) to evaluate the predictive power of some robust and regularization methods in trip generation modeling. Within this scope, approximately 49,000 valid Household Survey Data (HSD) from 2001 (base year) and 2015 (target year) were used to develop comprehensive trip generation models for the home-based hospital trips. Multiple linear regression (MLR), ridge regression (RR), Liu, least trimmed squares (LTS), least trimmed squares–ridge (LTS–R), and least trimmed square–Liu (LTS–L) methods were used for the analysis, and results were evaluated in terms of specialized mean squared error (MSE) and root mean square error (RMSE). As a result, LTS–R and LTS–L increased future predictive power by approximately 55% compared with OLS. This study fills an important gap in the literature in terms of not only the use of regularization and robust methods in trip demand modeling but also examining characteristics that affect hospital trip behaviors. In addition, the paper proposes a “Hospital Mobility Coefficient” projection that could be useful in demand estimation for future hospital trips.

Equitable distribution of bikeshare stations: An optimization approach

Bikeshare systems have attracted increased research interest ranging from bikeshare planning analyses to operational improvement studies (e.g., rebalancing, or station optimization). However, the interaction between bikeshare station spatial distribution and actual bikeshare activities when addressing equity issues has not been thoroughly considered. Moreover, there is a paucity of research helping governments develop incentive programs for equitable bikeshare services. To fill this research gap, we develop a model to estimate the potential demand (i.e., bikeshare trip production and attraction) and its distribution, and evaluate performance over a set of objectives (e.g., maximization of annual revenue, accessibility improvements) to find the most equitable distribution of stations. We build a genetic algorithm to solve this multi-objective optimization. The study uses the Divvy bikeshare system in Chicago as a case study, and compares the solutions of the model with the system's expansion (new stations added) in 2016, which targeted disadvantaged areas. When selecting accessibility as the main objective, the results indicate the need to provide more stations in disadvantaged areas and those results overlap with the system's expansion in 2016. On the contrary, the goal of revenue maximization results in a smaller network of stations and fewer accessibility improvements, especially in disadvantaged communities. A sensitivity analysis uncovers the greatest obstacle (i.e., station cost) to adding more stations in disadvantaged areas. More importantly, a Pareto frontier of this multi-objective optimization supports several policy suggestions for incentivizing private bikeshare companies to target more disadvantaged populations. Our results show the importance of considering accessibility and other equity constraints in developing a more inclusive, equitable and sustainable transportation system, and we provide several planning suggestions.

Evaluasi Kinerja Simpang Bersinyal Jl. Arief Rahman Hakim-Jl. Klampis Anom-Jl. Klampis Jaya Surabaya

The intersection between Arief Rahman Hakim Street, Klampis Anom Street, Klampis Jaya Street is a signalized intersection with a high-density level due to trip generation and attraction. The low level of knowledge and culture of traffic is the phenomenon of vehicle congestion at the intersection involving many actors as the cause. This study aimed to determine the traffic performance of the signalized intersection at the Arief Rahman Hakim Street, Klampis Anom Street, Klampis Jaya Street at this point as well as to predict the traffic performance of the signalized intersection on the Arief Rahman Hakim Street,Klampis Anom Street, Klampis Jaya Street in 2024. The results of the performance evaluation of the signalized intersection on the Arief Rahman Hakim Street, Klampis Anom Street, Klampis Jaya Street was for the standard degree of saturation received in 2021; it was 4.33 in the north, 0.23 in the south, 3.17 in the east, and 2.98 in the west. In 2024 it will be 4.96 in the north, 0.36 in the south, 3.49 in the east, and 3.41 in the west. In 2024 the rate of population growth and the need for private vehicles will increase greatly.

MODEL BANGKITAN DAN TARIKAN PERGERAKAN KAWASAN PERMUKIMAN DI BANDARLAMPUNG

Urban planning is a solution to problems that may arise as a result of the continued increase in the population of the city which is also associated with a variety of activities which then affect land use and urban mobility, these causing transportation problems to be identified at this time so that preventive actions can be planned for the future. In Bandarlampung as a Metropolitan City, it can be seen that the population is increasingly dense and its activities are increasingly diverse as seen on Jalan Urip Sumoharjo, which is surrounded by heterogeneous land uses like residential areas, trade and service areas and public service areas where Jalan Urip Sumoharjo is a strategic road for in Bandarlampung. So the purpose of this research are to identify the model of trip generation and trip attraction that determine the factors causing the movement of residential area of Jalan Urip Sumoharjo. The research method used are multiple linear regression and descriptive statistical analysis. Based on the analysis result, the factors that influence movement on Jalan Urip Sumoharjo are family members, number of working family members, average distance and time of movement, monthly income, vehicle ownership, purpose and travel destination, and accessibility.

Kontribusi Tarikan Perjalanan Kawasan Perdagangan Dan Jasa pada Jalan Menoreh Raya, Semarang

Due to the increasing of people’s needs for goods and service, it encourages the trip attraction of commercial land uses. Menoreh Raya Street is secondary arterial street which function as commercial land use. This commmercial land use conduce the trip attraction that contribute to traffic flows. Therefore, the calculation of trip attraction’s contribution is needed to anticipate the problem in future. The purpose of this study is to know the contribution of commercial land use’s trip attraction on one corridor and the contribution of commercial land use’s trip attraction per type. The methods that used in this study are calculate the trip attraction and the contribution of trip attraction. The results show that this commercial land use contributes 9,75% to traffic flows of Menoreh Raya Street on peak hour. The biggest contribution is attracted by variety store by 24,93%, high-turnover (sit-down) restaurants by 21,09%, and quality restaurant by 10,62%. Menoreh Raya Street’s function as connection between downtown and sub-urban makes the traffic flows are dominated by go home after work activity. Moreover, people tends to buy some daily needs and dinner on evening’s peak hour. This study could be used to know the contribution of traffic volume that attracted by commercial land use on secondary arterial street’s traffic flows that has same characteristic with Menoreh Raya Street. The contribution of trip attraction is useful to anticipate the commercial land use’s trip attraction that affect the increasing of street load and traffic jam.

Transportation Planning Using Conventional Four Stage Modeling : An Attempt for Identification of Problems in a Transportation System

In the formative development of urban transportation planning, travel forecasting is essential. Various travel simulation techniques are used to estimate possible traffic scenarios. It's been used to analyze road capability, transportation system developments, and new land use strategies and policies. The solution to travel decisions is generally obtained if the models are done in sequence. Many attempts are made to simulate the variety of choices made by travellers as a part of a response to a particular highway system, related transit system, and connected policies. For this purpose number of assumptions are considered regarding the decisions made by the people, factors considered by them, and their reaction as a response to the alternatives available in the transportation system. Generally, the process of travel simulation follows trip generating at the trip generation zone and moves through different links of the network and nodes. It ultimately ends at the trip attraction zone. The simulation method is known as Traditional Four Steps Transportation Modeling, and it includes the four basic models namely Trip Generation, Trip Distribution, Mode Choice, and Traffic Assignments. This paper it is tried to focus on the process of this traditional four-step transportation modeling system. The modeling method calculates a simplified transportation network from the city of Ulhasnagar, India.

Trip attraction model of selected zones in Baghdad city

The initiation of this analysis is to establish a mathematical model for predicting trip attraction for Bab al-moadam sector in Baghdad city that involving land use features. To achieve the study objective, the study area is divided into two zones. A conversation mechanism has been adopted to collect data. The data collection included all education centers, state institutions, shopping centers, and health centers, and their numbers were 21 educational centers, 10 centers belonging to state institutions, 5 shopping centers, 12 health centers. The trip attraction models for six purposes of trips(Educational, state institution, shopping, health, other trips, and the total trips) have been developed. These models have been built when the gathered data was fed to the (spss) software version 22 by using stepwise regression process. The results show that the attraction of trips is highly related to the number and diversity of vital centers in the region. we conclude from this study that educational trips depend on the number of students. Work t rips depend on the number of employees. The number of stores is the most influencing factor on shopping trips. Health trips depend largely on the number of beds in addition to the number of patients in consulting clinics and emergency clinics. Other trips are it depends on other health trips, other educational trips and other work trips, the number of total trips that are attracted to the study area depend on the number of employees, the number of health staff, the number of visitor, and the number of students.

Effects of built environment and weather on demands for transportation network company trips

This paper investigates the effects of the built environment and weather on the demands for transportation network companies (TNC) in Toronto. The research is based on a historical dataset of Uber trips from September 2016 to September 2018 in Toronto. A wide range of built environments, sociodemographic, and weather data are generated at the dissemination area-level and fused with the monthly aggregated Uber dataset. To provide insight into the underlying factors that affect TNC demand, a series of aggregate demand models are estimated using log-transformed constant elasticity demand functions, with consideration of the seasonal lag effect. To capture the weather effect, an autoregressive moving average model is estimated for the downtown core of Toronto. The model results show that the influence of lagged ridership and seasonal lag effect have a positive correlation with TNC demand. The trip generation and attraction models reveal that TNC trips increase where when the commuting trip duration is longer than 60 min. It is found that the number of apartments in a dissemination area is positively correlated with TNC trip generation, while the number of single-detached houses has a negative correlation. The time-series model indicates that temperature and total daily precipitations are positively correlated with TNC demand. Due to the lack of comprehensive data sources on the Uber and Lyft ridership, the policymakers often struggle to make evidence-based policy recommendations to regulate such disruptive technologies. The series of models presented in this study will help us better understand the potential users of transportation network companies (TNC) and the effects of land use, built environment and weather on transportation network company trips.

Urban Traffic Planning and Traffic Flow Prediction based on ulchis gravity model and Dijkstra algorithm

Traffic flow forecast is the basic content of urban traffic planning, its planning will play an important role in the development of small towns. Therefore, this paper takes Nanliang town as an example, analyzes the topography of Nanliang Town, and then makes planning on the functional zoning and spatial structure, the layout of various types of land and road network, On this basis, the “four stage” demand forecasting method is used to study the development of Nanliang town’s traffic demand. The original unit method and ulchis gravity model are used to predict the trip generation, the trip attraction and the distribution of traffic volume. The Dijkstra algorithm is used to calculate the shortest path. After that, the all-or-nothing assignment method and the improved multi-path Logit method were respectively adopted to allocate the space OD amount of various travel modes to the specific road network, and the traffic flow distribution results under the two allocation methods are obtained respectively. According to the distribution result chart, it can show that the improved multi-path Logit method is closer to the reality, it can provide theoretical support for the study of urban traffic demand.

Trip Attraction Models, Using Regression Analysis, for Shopping Malls in Lebanon

Trip Attraction Models, Using Regression Analysis, for Shopping Malls in Lebanon

Perubahan Fungsi Hunian Menjadi Komersial Terhadap Tarikan Perjalanan Di Perumnas Bumi Tlogosari, Kota Semarang

The urban problem that is often experienced by developing countries is the high change of land use in a housing into commercial land. It happens because people often use existing facilities in urban areas regardless of their carrying capacity. The impacts that can be caused generally affect the capacity of transportation facilities and infrastructure. Therefore, changes in land use can affect trip generation. One of the transportation analyzes related to trip generation is trip attraction. The sample location chosen is in the Tlogosari Bumi Housing which has the characteristics of land use change from residential to multifunction land use. There are two types of data used, namely primary data and secondary data. The method used is descriptive analysis, and multiple linear regression analysis. The results of this study indicate that the attraction of travel on Tlogosari Raya street is influenced by the number of employees. The recommendation that needs to be done is to enforce permits to control land use change.

Complexity of small-scale commercial area on Menoreh Street in Semarang and its contribution to trip attraction model

The various small-scale commercial create complexity in estimating the travel impact, especially the congestion on collector roads that connect city center and sub-city center. The purpose of this study is to understand the complexity of commercial land use through trip attraction model and its contribution to traffic flow on Menoreh Street as one of the collector roads in Semarang City. The method that used are contribution trip attraction by classification of small-scale commercial activities and multiple linear regression with dependent variable Y is the trip attraction of small-scale commercial area (pcu/hour) and the independent variables are X1 (store area), X2 (parking area), X3 (sales income), X4 (number of employees), and X5 (store operational hours). There are thirteen classification which apparel store has a biggest contribution and car wash has a lowest contribution to trip attraction. From that classification, there are five different model which number of employees and sales income influence the trip attraction. The model can estimate trip attraction in the small-scale commercial area that is growing rapidly in the Semarang City so that it can anticipate the transportation problems.