Trip Generation Research Articles

A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

PROBABILITAS BANGKITAN PERJALANAN BERBASIS AKTIVITAS DARI KAWASAN PERUMAHAN DI KOTA KUPANG

<p class="11daftarpustaka">Generally, trip generation is based on activities from the household. Each household member, of course, has different travel destinations in a household, there will be several trips that are caused by the activity. This study is aimed at determining the factors that influence the trip generation and predict the magnitude of trip generation originating from residential areas based on activities in Kupang city. Travel activities were divided into three categories i.e. work activities, school activities and other activities. Data collection was done by distributing questionnaires to 444 households through random sampling technique. Based on the calibration of the modelling results with multinomial logit regression analysis, the factors that significantly influence trip generation from residential areas were the number of family members who did activities, the average distance travelled and the average travel time. The probability of trip generation was predicted to be 55.48% for work activities, 25.61% for school activities and 18.90% for other activities.</p>

Freight and Service Parking Needs in Historical Centers: A Case Study in São João Del Rei, Brazil

The objective of this study is to analyze the demand for loading and unloading parking spaces in the center of São João Del Rei, a historical city in the State of Minas Gerais, Brazil, through freight trip generation models. To generate the models, the number of employees is used as an independent variable. Results show that the historical center receives an average of 710 freight trips per day, which would require at least 43 spaces for loading and unloading. As the center has only eight such spaces available, representing 18% of total demand, this study proposes new locations and suggests transportation demand management measures that could be used in conjunction with the allocation of new parking spots.

The Study Degree of Saturation on Toll Road Access Based on Changes in Urban Settlement Land

Changes in land use that are not in harmony with the growth in the number of vehicles cause congestion and decrease in the speed of travel. Toll road access is one of the important ways to decrease traffic jams in cities that require smooth traffic flow. The purpose of this study is to analyze the level of service level of toll road access to land use changes from residential environments to commercial areas. The analytical method was used to use multiple regression analysis to estimate traffic generation from changes in commercial land use. The degree of saturation of road toll access before land use change shows DS values of 0.56 to 0.65 which were analyzed at peak morning hours. This was an indication of the smooth flow of traffic. After there was a change in land use shown from the trip generation from the commercial area with the equation Y = 2.554 + 1.143 X3 + 1.041 X4 + 1.011 X5 +1.256 X6 + 1.2045 X7 with the significance of the model R2 = 0.91. So that due to changes in land use, the toll access point was higher with changes in the degree of saturation 0.6 to 1.2.

Study of Standard Trip Attraction Models of Various Land Use in the Surakarta City

The law states that every activity centre, settlement and infrastructure development plan that causes disturbance to safety, order and smoothness of traffic and road transport must be carried out traffic impacts analysis. The magnitude of traffic impact that occurs due to the building construction is influenced by the amount of trip generation and attraction that generated by the building activities. Standard value of trip generation and attraction of certain land use is important to be used as a reference in calculating performance of road network. The aim of study is to determine the trip attraction model of various land use including hotel and minimarket in the Surakarta City. Modelling analysis requires data such as vehicle volume in and out of land use and land use parameter such as the number of rooms for hotel and size of building floor area for minimarket. Based on these data then a modelling analysis is carried out using regression analysis to obtain a land use trip attraction model. The result of analysis shows that regression trip attraction models of 3-star hotel, 4 and 5-star hotels and minimarket are Y= 0.1123*X + 12.55; Y = 0.2853*X + 11.017 and Y = 0.2444*X + 0.5623 consecutively.

Institute of Transportation Engineers Guidelines versus Actual Trip and Parking Generation for a Transit-Oriented Development in an Auto-Oriented Region

The Institute of Transportation Engineers (ITE) guidelines serve as the most widely used reference for trip and parking generation estimates for any new development in the U.S.A. However, recent empirical studies question the efficacy of ITE guidelines in forecasting trip and parking generation in transit-oriented developments (TODs). Following the methodology of studies on seven TODs across the U.S.A., this study focuses on Dallas (TX), as a more auto-oriented American city, to explore the trip and parking generation at Mockingbird TOD. It finds that, with the exception of Station Park in Salt Lake City (which is really a transit-adjacent development rather than a TOD), the Mockingbird TOD has the lowest walk mode share (13.6%), the lowest bike mode share (0.22%), the lowest bus transit mode share (1.09%), and by far the lowest rail transit mode share (5.9%) of all the other seven TODs. Similarly, the Mockingbird TOD ranks first in relation to the driving mode share, with about 80% of its daily trips generated by driving. This is almost twice as many driving trips as the average of the other six TODs, which makes Mockingbird an auto-oriented TOD located in an auto-oriented region where more than 96% of commuting trips are by automobile. Still, the total automobile trip generation rate in Mockingbird is about 12% lower than the ITE estimates. Similarly, while the parking supply in Mockingbird TOD is less than 48% of the ITE recommended supply rate, its peak parking occupancy is only about 55% of the TOD supply.

Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.

Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling

Traditional approaches to travel behaviour modelling primarily rely on household travel survey data, which is expensive to collect, resulting in small sample sizes and infrequent updates. Furthermore, such data is prone to reporting errors which can lead to biased parameter estimates and subsequently incorrect predictions. On the other hand, mobile phone call detail records (CDRs), which report the timestamped locations of mobile communication events, have been successfully used in the context of generating travel patterns. However, due to their anonymous nature, such records have not been widely used in developing mathematical models establishing the relationship between the observed travel behaviour and influencing factors such as the attributes of the alternatives and the decision makers. In this paper, we propose a joint modelling framework that utilises the advantages offered by both travel survey data and low-cost CDR data to optimise the prediction capacity of traditional trip generation models. In this regard, we develop a model that jointly explains the reported trips for each individual in the household survey data and ensures that the aggregated zonal trip productions are close to those derived from CDR data. This framework is tested using data from Dhaka. Bangladesh consisting of household survey data (65,419 persons in 16,750 households), mobile phone CDR data (over 600 million records generated by 6.9 million users), and aggregate census data. The model results show that the proposed framework improves the spatial and temporal transferability of the joint models over the base model which relies on household travel survey data alone. This serves as a proof-of-concept that augmenting travel survey data with mobile phone data holds significant promise for the travel behaviour modelling community, not only by saving the cost of data collection, but also improving the prediction capability of the models.

Smart transportation planning: Data, models, and algorithms

By developing cities and increasing population, smart transportation becomes an essential component of modern societies. Extensive research activities using machine learning techniques and several industrial needs have paved the way for the emerging field of smart transportation. This paper presents data, methods, and models that are essential for intelligent planning of transportation. In particular, the current data sources for gathering information to control or forecast traffic are described, connected Vehicles (CVs) that bring smart and green transportation to modern life is also discussed. Clustering Analysis as an effective unsupervised machine learning method in trip distribution and generation and traffic zone division is discussed in the paper. Various machine learning techniques and models that use time series prediction are introduced in this paper including ARIMA, Kalman filtering, Holt winters'Exponential smoothing, Random walk, KNN Algorithm, and Deep Learning. Finally, a discussion on the main advantages and drawbacks of these models, as well as the business adoption of the forecasting models are presented.

Spatial associations of dockless shared e-scooter usage

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

WALKING TRIP GENERATION AND BUILT ENVIRONMENT: A COMPARATIVE STUDY ON TRIP PURPOSES

In recent decades, enhancing the share of walking in individuals’ daily trips has become a priority for transportation policymakers, urban planners and public health researchers as an interdisciplinary area. In this regard, determining influential factors on walking has become a matter of contention to move toward a sustainable mode of transportation. This study investigates the impact of the influencing factors on the share of walking in trip generation from/to Traffic Analysis Zones (TAZ) of a city across four trip purposes. In this study, individuals’ trip information for four trip purposes has been tested in order to detect influencing factors on walking trip generation based on 112 TAZs for the city of Rasht, Iran. According to the results, density and design are found to be more influential for produced walking trips and diversity is shown to be more effective for attracted walking trips.

Potential of Sea Passenger Movement Post Ports Development in Kepulauan Seribu

This study aims to determine the potential movement of sea transport passengers in Kepulauan Seribu. This study used a quantitative approach and a descriptive method with a multiple linear regression analytical technique on transportation modelling. The data that is used for analytical based on data survey of 300 respondents that is randomly distributed. Based on the calculation of the trip generation and trip attraction of passengers and goods in each zone in Kepulauan Seribu Regency, it can be seen that the biggest trip generation and pull is in the Jakarta Zone. This is because the zone is a large city which is the center of activity of Kepulauan Seribu Regency Sea transportation so that most of the movement towards and originating from the Jakarta Zone. It is known that the largest value obtained from the OD Matrix for the next 20 years is 6,970 people per day for the movement from the Pulau Kelapa Zone to the Jakarta Zone, while the movement from the Jakarta zone to the Pulau Kelapa Zone is 6,970 people per day. Based on the results of this study it can be seen that the level of passenger travel for sea modes found in Kepulauan Seribu Regency has the potential to increase significantly.

Trip Generation Rates of Land Uses in a Developing Country City

In recent decades, a major shift in the land use pattern has been observed in Dhaka, the capital city of Bangladesh. To understand and model the impact of these land use changes on transportation demand, this study aimed to determine the trip generation rates for six different land use categories adjacent to Mirpur Road in Dhaka. A total of 20 establishments consisting of six land use categories were selected for the collection of data on person trip rates and respective modal share by manual counts and intercept surveys. These data were used to develop vehicular trip generation rates for each land use category in passenger car equivalents as a uniform unit of comparison. Results showed that commercial and healthcare land uses had the highest average and peak-hour trip rates. There was also a significant variation in the share of eight transport mode categories among the trips generated by the land uses. The peak-hour trip generation rates of the study area were found to be different from the values established by the Institute of Transportation Engineers which corresponds to the fact that trip generation depends on a host of factors, such as surrounding land uses, modal share, the economic condition of a region, and so forth, rather than on a single factor inherent to the land use. The findings of this research can help to determine the trip generation impact of new establishments and consequently identify suitable locations to minimize the impact.

Development of Trip Generation Model: Case Study of Makarpura GIDC

Transportation planning is a basic piece of transportation designing, which incorporates four fundamental procedures viz, trip generation, trip distribution, model split, and trip assignment. This paper focuses on the trip generation, which is one of the four basic transportation planning processes. Due to Urbanization and Industrialization freight movement plays a very vital role in the Indian scenario. Freight transportation is considered as a backbone of industries and its development as it is essential for bringing raw material and delivering finished goods to the market. Makarpura GIDC is a part of the industrial assets of Vadodara city, which generates huge freight trips in Gujarat. The traffic volume count survey has been conducted which includes all the routes of Makarpura GIDC, for prediction of future trips generation. Paper also includes a detailed classification of industries and various trip generation models by the multiple linear regression method, which takes into consideration particular types of industries. Also, a relationship between the dependent and independent variables has been derived from the scatter diagram.

Estimate Attraction Rate for Shopping Centers

Abstract In developing countries like Baghdad, it has seen high growth of urbanization accompanied by the high trip rate, which conduces increased vehicle traffic and human traffic. One of the major problems is the increase in traffic congestion of unplanned development of various structures at various locations. Four-step demand models are important in planning and forecasting future demand. Trip generation is an important step when planning urban infrastructure. In this study, three shopping centers (AL Mansour mall, Babylon mall, and al Waha mall) were selected, trip attraction rates were calculated using two approaches (trip rate analysis method, and Regression analysis method). The number of persons entering and exiting the site for every 15-minute interval during peak hours is estimated. This research considers various physical characteristics of shopping centers, gross floor area (GFA), number of employees and the number of stores in the shopping center. From the data analysis, good relations exist between the dependent variable (trip rate) and various independent variables, and the developed trip rate per GFA per 1000 m2 is 133 person trips and various trip rates are presented through this study.

Improving transportation impact analyses for subsidized affordable housing developments: A data collection and analysis of motorized vehicle and person trip generation

Transportation impact analyses begin with a trip generation estimation process—estimating motorized vehicle and person trip counts coming and going from the proposed site. Data commonly used is often insensitive to urban contexts (such as employment densities) and socioeconomic conditions. This insensitivity results in sometimes exaggerated estimates, an increase associated transportation impact fees, and a need for additional mitigation of impacts which may further hinder land development. In this study, we collected and analyzed person and motorized vehicle count data from 26 affordable housing developments in Los Angeles and San Francisco. Counts were regressed upon site and built environment characteristics known to influence site-level travel behavior (e.g., parking supply, employment density), and regressions were validated using externally collected data. The findings indicate the average square footage of dwelling units, parking ratios, and nearby retail employment densities to be important predictors. The findings also indicate that increasing the parking supply from one space to two for each dwelling unit will result in a significant predicted increase of approximately 0.26 and 0.18 motorized vehicle trips per dwelling unit for AM and PM peak periods, respectively. These findings reiterate the need for trip generation methodologies sensitive to the built environment and sociodemographics.

Modeling commercial vehicle trip generation at the business-establishment level

The planning and design of efficient transportation systems require an in-depth understanding of the micro-behaviour governing vehicle movements. Recent efforts to analyze commercial vehicle movements have begun focusing on the collection and usage of detailed microdata sets. This paper contributes to these efforts by devising statistical models for predicting the number of outbound commercial vehicle trips at the firm level given a sample of establishments in the Windsor, Ontario region. A comparison was made between a model that can be created using microdata obtained through an establishment survey, a model that utilized commercial firm lists with basic firm attributes that include employment size and industry classification, and a modified model with the basic variables and limited detailed microdata. The results suggest that commercial firm datasets can be used to generate reasonable predictions. However, additional information about employment, industry, vehicle ownership and other firm characteristics certainly enhance the models’ predictive ability.

A Multiclass Tour Flow Model and Its Role in Multiclass Freight Tour Synthesis

This paper summarizes the research conducted by the authors concerning the development of an analytical freight demand model that explicitly considers freight pickup and delivery flows by industry sector, the multiclass tour flow model (MC-TFM). The mathematical properties of the resultant model are investigated, including the convexity of the optimization model, the interconnection between the MC-TFM and the family of gravity models, and the MC-TFM's elasticities. The MC-TFM is then integrated into a multiclass freight tour synthesis model (MC-FTS) that is capable of inferring freight tour demand on the basis of secondary data such as traffic counts and estimates of freight trip generation by industry sector. The ability of the resulting MC-FTS is tested by means of numerical experiments involving test cases.

Impact assessment of short-term management measures on travel demand

Travel Demand Management (TDM) can be considered as the most viable option to manage the increasing traffic demand by controlling excessive usage of personalized vehicles. TDM provides expanded options to manage existing travel demand by redistributing the demand rather than increasing the supply. To analyze the impact of TDM measures, the existing travel demand of the area should be identified. In order to get quantitative information on the travel demand and the performance of different alternatives or choices of the available transportation system, travel demand model has to be developed. This concept is more useful in developing countries like India, which have limited resources and increasing demands. Transport related issues such as congestion, low service levels and lack of efficient public transportation compels commuters to shift their travel modes to private transport, resulting in unbalanced modal splits. The present study explores the potential to implement travel demand management measures at Kazhakoottam, an IT business hub cum residential area of Thiruvananthapuram city, a medium sized city in India. Travel demand growth at Kazhakoottam is a matter of concern because the traffic is highly concentrated in this area and facility expansion costs are pretty high. A sequential four-stage travel demand model was developed based on a total of 1416 individual household questionnaire responses using the macro simulation software CUBE. Trip generation models were developed using linear regression and mode split was modelled as multinomial logit model in SPSS. The base year traffic flows were estimated and validated with field data. The developed model was then used for improving the road network conditions by suggesting short-term TDM measures. Three TDM scenarios viz; integrating public transit system with feeder mode, carpooling and reducing the distance of bus stops from zone centroids were analysed. The results indicated an increase in public transit ridership and considerable modal shift from private to public/shared transit.

Links between freight trip generation rates, accessibility and socio-demographic variables in urban zones

This paper proposes an assessment of the links between freight trip generation (FTG) rates and accessibility. First, the paper overviews the background, sets the context and motivates the research. Second, it presents the proposed methodology, which combines an FTG model, two accessibility indicators and a linear regression analysis to assess the relationships between freight trip demand and a set of socio-demographic variables including accessibility. The FTG modelling framework, adapted from previous works, allows estimating the number of freight trips with a small amount of standard data, even when no surveyed data is available. The two gravity accessibility indexes, one potential and one exponential, are defined in the continuity of recent freight accessibility works. To those indicators, a set of socio-demographic variables, including population, area or a zone (or density), are introduced. The relationships between FTG and all those variables are assessed via standard linear regression methods completed by the verification of the corresponding linear relationship hypotheses. Third, the framework is applied to the urban area of Lyon (France), where no urban goods survey data is available. Results show that potential accessibility seems to have a better correlation to FTG and could be a good decision support indicator when combined with the population as an explanatory variable. The population can be added to accessibility as an explanatory variable, the resulting models with two variables have a slightly lower accuracy but remains close to that of models with only accessibility as an explanatory variable. This work remains exploratory and finishes by proposing practical implications and further development lines.