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Developing Trip Generation Rates for Restaurants in Amman

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Ayat Al-Madadha at Hashemite University
Ayat Al-Madadha
Rana Imam at University of Jordan
Rana Imam
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Predicting the magnitude and pattern of vehicle movements for different trip purposes is essential for urban planning in order to avoid traffic congestion and ensure the reduction of travel times on the network. This study addresses one aspect of trip making that is restaurant trips in Amman and aims to develop trip generation rates based on observing travel behavior at various restaurants through statistical analysis. The main goal of this study is to determine the number of vehicles generated, attracted to and/or produced by restaurants in Amman, the capital of Jordan, by analyzing the effect of several explanatory variables on the number of generated trips. The explanatory variables include: parking capacity, gross floor area, number of employees, and number of seats available. The restaurants are divided into five main categories, namely: traditional, tourist, international, fast food, and mixed (restaurants and cafés). Two questionnaires were conducted. The first to determine the peak periods and days for data collection; while the second was for collecting data about restaurants in Amman (like type, size, number of employees, and parking capacity). The data collection included manual counts of the number of vehicles at each restaurant over the specific peak periods for each restaurant category dictated by the first questionnaire. Analysis of the second questionnaire showed that the generated trips to restaurants in Amman are mainly related to two variables; parking capacity and number of employees. Finally, the rates developed in this research are compared to available rates in the trip generation manual of the Institute of Transportation Engineers (ITE) and other regional manuals; to provide transport planners and decision makers with a reliable tool to predict future growth and help guide their decisions.
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International Journal of Information, Business and Management, Vol. 12, No. 2, 2020
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DEVELOPING TRIP GENERATION RATES FOR RESTAURANTS IN
AMMAN
Ayat Al-Madadhah1 and Rana Imam2
1MSc in Transportation Engineering, Department of Civil Engineering, The University of Jordan,
Amman-Jordan.
Email: ayatt@hu.edu.jo
2Associate Professor, Department of Civil Engineering, The University of Jordan, Amman-Jordan.
Email: r.imam@ju.edu.jo
Abstract
Predicting the magnitude and pattern of vehicle movements for different trip purposes is essential
for urban planning in order to avoid traffic congestion and ensure the reduction of travel times on the
network. This study addresses one aspect of trip making that is restaurant trips in Amman and aims to
develop trip generation rates based on observing travel behaviour at various restaurants through statistical
analysis.
The main goal of this study is to determine the number of vehicles trips generated, attracted to
and/or produced by restaurants in Amman, the capital of Jordan, by analysing the effect of several
explanatory variables on the number of generated trips. The explanatory variables include: parking capacity,
gross floor area, number of employees, and number of seats available. The restaurants are divided into five
main categories, namely: traditional, tourist, international, fast food, and mixed (restaurants and cafés).
Two questionnaires were conducted. The first to determine the peak periods and days for data
collection; while the second was for collecting data about restaurants in Amman (type, size, number of
employees, and parking capacity). The data collection included manual counts of the number of vehicles at
each restaurant over the specific peak periods for each restaurant category dictated by the first questionnaire.
Analysis of the second questionnaire showed that the generated trips to restaurants in Amman are mainly
related to two variables; parking capacity and number of employees. Finally, the rates developed in this
research are compared to available rates in the trip generation manual of the Institute of Transportation
Engineers (ITE) and other regional manuals; to provide transport planners and decision makers with a
reliable tool to predict future growth and help guide their decisions.
Keywords: trip generation; transportation planning; trip generation manual; restaurant trips; trip purpose.
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1. Introduction
The Urban Transportation Modelling System (UTMS) is known as the 4-step model. It
consists of four major steps; trip generation, trip distribution, mode choice and traffic assignment.
Trip generation is defined as the total number of trips generated by household in the zone, either
home-based or non-home-based. (Ortuzar & Willusmen, 2001). Home – based trips are where the
home of the trip maker is either the origin or the destination of the journey (Ortuzar & Willusmen,
2001). A non-home-based (NHB) trip conversely is one where neither end of the trip is the home of
the traveller (Ortuzar & Willusmen, 2001).
This study aims to build mathematical models for the trip generation rates of restaurants in
Amman, and to develop relationships between trip productions and attractions for each restaurant
type. These developed models could be used in the future to estimate the number of trips generated
or produced by similar restaurants under a new set of land use conditions. This research also
compares the values developed with the available rates in the trip generation manual of the ITE and
other regional manuals; to provide transport planners and decision makers with a reliable tool to
predict future growth and help guide their decisions.
Amman has a population of 4,327,800 inhabitants (DOS, 2018). This continued growth
adds to the traffic load on the road network leading to further congestions and delays in Amman.
This study is focused on restaurants trips in Amman, since this sector has also witnessed substantial
growth. As of 2019, the total number of restaurants in Amman is 5112 classified as shown in Table
1. For example, the tourist restaurants increased from 576 restaurants at 2011 up to 804 restaurants
in 2019 (GAM, 2019).
Table 1: Types of Restaurants in Amman
Type of Restaurant
Total Number in 2019
Tourist
804
Traditional
3520
International
507
Fast food
122
Mixed (Restaurant/Café)
159
2. Literature Review
The literature includes several international studies on restaurants, but none were
for Jordan. Previous international and local studies on trip generation are explored in this
section. To start with, Oliveira et al. (2017) presented a freight trip generation model for
food and beverage in Belo Horizonte, Brazil. The methodology adopted in this study was
a questionnaire to obtain freight flow data; then build and calibrate the model using the
collected data; and finally geographically analyse the results to understand the sector’s
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impact on urban traffic. The survey was conducted in 300 establishments and used
Geographic Information System (GIS) tools to compute a continuous surface by
interpolating the discrete values of travel per day delivered from the trip generation
model. A structured questionnaire was designed to obtain data on goods, frequency,
operational time, and location of loading/unloading of goods. The independent variables
were: area of the establishment; number of employees; and operation day of the
establishment.
Datta et al. (1998) developed a trip-generating database of multiuse commercial
developments in Michigan, USA. The models could be used for investigating future trip generation
for future multiuse developments which include gasoline stations with convenience stores and fast
food restaurants. These developments were found to be particularly desirable to customers with two
or more travel needs.
Al-Zahrani and Hasan (2008) studied fast food restaurants in Jeddah, Saudi Arabia. A
sample size of twenty fast food restaurants was selected for this study. The studied restaurants were
grouped into two categories: hamburger chain restaurants, and chicken chain restaurant. As for the
data collected in this study, it was grouped into two categories: office data and field data. Office
data was collected from managers about the area and the number of employees at the facility. The
field data included the number of parking stalls available at the facility, the availability of drive-
through windows, and traffic volume data.
A study was conducted about restaurants in Malaysia by Abdulameer et al. (2014). Ten fast
food restaurants, belonging to three international chains with drive-through windows, were studied
in detail. Trip rates were related to the number of parking spaces, gross floor area, and number of
seats in the restaurants. Field surveys were conducted, noting the arriving and departing vehicles
using Automatic Traffic Counters (ATCs) and were verified through video data and short-term
manual counts. The following three parameters were considered: gross floor area, number of
parking spaces available, and number of seats in the restaurant. The developed linear regression and
multiple regression models showed that the gross floor area and the available number of parking
spaces were significant parameters in determining the number of trip generations and, the number
of seats in Malaysia was not related to the number of trips due to climatic conditions.
Quintero et al. (2016) developed trip generation rates by mode for 3 school types (private
schools, semi-private and public schools) in Merida-Venezuela. Their analysis focused on trip
generation as a function of the social and economic attributes of households. Using stepwise
regression and transformation of data, they found highly correlated models.
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Salasa and Rood (2017) studied trip generation and parking demand of mosques in the
greater Cape Town area of the Western Cape. The mosques surveys were conducted and
relationships between the number of vehicles generated and parked at the mosques with the
characteristics of the mosques and their surrounding areas were determined. The results contribute
to the understanding of the traffic characteristics of mosques, but should not be readily applied to
other metropolitan areas without taking into consideration local travel demand and mode choice
characteristics.
Al-Masaeid and Fayyad (2018) developed trip generation rates for residential areas in Irbid,
Jordan. Household surveys were carried out to collect data on trips. A sample of about 2500
households was interviewed, and regression analysis and cross-classification approach were both
used to model the trip generation rates. Family size, car ownership and income levels were used in
their approach. During workdays, the analysis indicated that the number of home-based work trips
constituted about one-third of the total home-based trips.
Al-Masaeid et al. (2018) estimated trip and parking generation of shopping centres in
Jordan. The study investigated 28 shopping centres located in three major cities during peak
periods. The trips generated and parking demand were found to be strongly related to the gross
floor area, number of employees, availability of cinemas, and sufficient parking supply in shopping
centres.
3. Research Methodology
This study is focused on developing trip generation rates for restaurants in Amman. The
sample size of the study was taken as 177 restaurant distributed in the 5 categories as described in
Table 2. Data was collected three times for each restaurant to cover all meals: breakfast, lunch, and
dinner.
Table 2: Distribution of the Study Sample
The methodology followed in this study relied mainly on two questionnaires. The first
questionnaire consists of two sections: the first section measures the number and percentage of
visitors per day, and the second section measures the number and percentage of visitors per meal.
Category
Population
Confidence
Level
Sample Size
Needed
Sample
Size Used
Traditional
Fast food
Tourist
International
Mixed (Restaurant /Café)
1000
500
1000
40
800
95%
95%
95%
95%
95%
24
23
23
15
23
45
57
30
15
30
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The second questionnaire consists of two parts as well. The first part collects data about the
restaurants (category, rank or rating, number of employees, location, parking availability, number
of seats, drive-through availability, valet service availability, and floor area in squared meter),
while the second part collects data on rush hours, vehicle types, restaurant infrastructure, number of
customers, and the street network. The other type of data is the traffic volume for vehicles entering
and exiting the restaurants which were recorded manually at each entry and exit point of restaurants
for three days during weekends. The counts were carried out during summer months when the
generated restaurants trip rates are expected to be at maximum levels to reflect the most critical
traffic movement since it is season of school holidays, pleasant weather, and presence of tourists
and visiting Jordanian expatriates in the kingdom. The methodology plan followed in this study is
summarized in Figure 1.
Figure 1: Study Plan Methodology
4. Analysis of Results
Linear Regression was used to produce restaurants trip generation models that best predict
the number of trips generated to restaurants based on parking capacity, gross floor area (m2),
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number of seats, and number of employees. The most commonly used method for developing trip
generation rates is regression, relates a response to a set of predictor independent variables in order
to develop a prediction equation (model) with the least possible error of prediction. The statistics
were used at a confidence level of 95% to evaluate the statistical significance of the coefficients
associated with the different variables used in developing the equations. Analysis of the first
questionnaire indicates the peak day and time period per meal as shown in Table 3, except for fast
food. Survey results for the fast food restaurants category show that the meals distributions over
days and hours of the week are slightly different for lunch over Fridays, but the survey data for
breakfast and dinner for the fast-food category matches other categories.
Table 3: Survey Day of Week and Time per Meal
Meal
Day of Week
Time
Breakfast
Lunch
Dinner
Friday
Saturday
Thursday
8:30-10:30 am
1:30-3:30 pm
9:00-11:00 pm
The results of the second questionnaire provide the arithmetic means and standard
deviations of the respondents’ answers and present the respondents’ level of agreement with the
listed factors affecting restaurant trip generating in the questionnaire as shown in Table 4. In order
to compare the arithmetic mean of the responses according to the scale of the questionnaire the
five-point Likert scale was adopted. The arithmetic mean default is equivalent to the value of (3)
for the interpretation of the answers, and the responses of the respondents of the study sample were
assessed as follows:
• Arithmetic mean is 5-3.67 means: high level of agreement.
• Arithmetic mean is 3.66-2.67 means: medium level of agreement.
• Arithmetic mean is less than 2.66 means: low level of agreement.
Table 4: Arithmetic Mean, Standard Deviation, and Agreement Level of Study Dimensions
No.
Dimension
Arithmetic
Mean
Average
St. Deviation
Level
1
Seasonal, weekly, and temporal variations
3.76
0.94
High
2
Access mode: private transport, public transport, or walking
3.77
0.95
High
3
Parking availability and capacity
3.88
0.96
High
4
Valet service and location of restaurant
3.89
0.94
High
5
Street network and conditions around the restaurant
3.64
0.95
Medium
By testing the study’s major hypothesis that there is no effect of the five study dimensions
(listed in Table 4) on the generated trips to restaurants in Amman. If the null major hypothesis is
rejected, then the alternative hypothesis is accepted: meaning that there is an effect of the study
dimensions on the generated restaurants trips in Amman. To start with, models using a single
variable to predict the number of trips generated are needed. In order to ensure the accuracy of the
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trends, the standard deviation of the average number of trips was calculated and presented in Table
5.
Table 5: Standard Deviation of Average Trips
Type of restaurant
Thursday
(9:00-11:00 pm)
Saturday
(1:30-3:30 pm)
Friday
(8:30-10:30 am)
International
Tourist
Traditional
Fast Food
Mixed
20.66
31.825
40.54
57.55
24.66
15.33
15.58
24.411
29.31
13.23
N/A
N/A
67.75
N/A
7.81
Tables 6-10 summarize the results of the single variable models for restaurants trips in
Amman during the peak periods for all restaurants categories. Table 11 provides a summary of the
chosen models for each restaurant category.
Table 6: International Restaurants Prediction Models
Table 7: Tourist Restaurants Prediction Models
Table 8: Fast Food Restaurants Prediction Models
Variable
Dinner Model
(9:00-11:00 pm)
Adjusted R2 for Dinner
(9:00-11:00 pm)
Lunch Model
(1:30-3:30 pm)
Adjusted R2 for Lunch
(1:30-3:30 pm)
Parking capacity
Gross floor area (m2)
Number of seats
Number of employees
y = .9583x-29.483
y = -.026 x +27.861
y= -0.197x+63.194
y =-0.301 x +45.67
R2= 0.7744
R2= 0.1456
R2= 0.0872
R2= 0.0139
y = 0.675x -16.55
y =-0.041x + 42.216
y =0.52x + 31.67
y =-0.130x+47.627
R2 = 0.6978
R2 = 0.2028
R2 = 0.3126
R2 = 0.0692
Variable
Dinner Model
(9-11 pm)
Adjusted R2 for Dinner
(9-11 pm)
Lunch Model
(1:30-3:30 pm)
Adjusted R2 for Lunch
(1:30-3:30 pm)
Parking capacity
Gross floor area (m2)
Number of seats
Number of employees
y = .04191x-7.303
y = -0.0232 x +72.273
y= 0.1057x+28.2
y =0.5018 x +22.142
R2= 0.7728
R2= 0.0213
R2= 0.1389
R2= 0.6805
y = 0.2043 x -8.129
y =-.0194 x +48.651
y =.0589 x +22.696
y =0. 2048 x+26.071
R2 = 0.7662
R2 = 0.0619
R2 = 0.0802
R2 = 0.4728
Variable
Dinner Model
(9-11 pm)
Adjusted R2 for Dinner
(9-11 pm)
Lunch Model
(1:30-3:30 pm)
Adjusted R2 for Lunch
(1:30-3:30 pm)
Parking capacity
Gross floor area (m2)
Number of seats
Number of employees
y =0.5197 x +48.523
y =0.76 x +75.87
y=0.9022x-23.562
y =0.5924 x +118.93
R2 = .3743
R2 = 0.0712
R2 = 0.5838
R2= .0434
y =0.179 x +53.776
y =0.0487x +54.778
y =0.451 x +9.0821
y =0.2329 x +78.744
R2 =0.2017
R2 = 0.1329
R2 = 0.6625
R2 = 0.0304
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Table 9: Traditional Restaurants Prediction Models
Dinner Model
(9-11 pm)
Adjusted
R2
Lunch Model
(1:30-3:30 pm)
Adjusted
R2
Breakfast Model
( 8:30-10:30 am)
Adjusted
R2
y =0.2454x +71.231
y =-0.028x+78.053
y =-0.044x+66.783
y=-0.425x+54.31
R2=0.128
R2=.027
R2=.0239
R2=0.023
y=0.632x+56.423
y=0.030x+52.372
y=0.096x+47.128
y=0.4152x+38.81
R2=0.111
R2=0.0396
R2=.01468
R2=0.7153
y =0.889x+44.423
y =0.0176x+63.99
y =0.4265x+16.55
y =.0968x+72.365
R2=.0285
R2=.0007
R2=.1388
R2=.0019
Table 10: Mixed Restaurants Prediction Models
Dinner Model
(9-11 pm)
Adjusted
R2
Model for Lunch
(1:30-3:30 pm)
Adjusted
R2
Model for Breakfast
( 8:30-10:30 am)
Adjusted
R2
y=0.2532 x +14.6
y=0.041x +22.831
y=0.1603x+12.159
y=-0.688x+76.618
R2=0.4811
R2=0.4349
R2=0.3635
R2=0.095
y=0.0598x+23.86
y=0.0299x+14.14
y=0.1026x+9.452
y=0.522x+54.282
R2=0.0933
R2=0.8027
R2=0.5154
R2=0.1903
y=0.0404x+2.509
y=0.0176x+2.550
y=0.0715x-7.6971
y=-0.0101x+8.041
R2=0.122
R2=0.797
R2=.7186
R2=.0002
Table 11: Summary of Models
Type of restaurant
Best Model for Each Meal
Dinner Lunch Breakfast
International
Parking capacity
y = .9583x-29.483
Parking capacity
y = 0.675 x -16.55
Not Available
Tourist
Number of employees
y =0.5018 x +22.142
Number of employees
y =0.2048 x+26.071
Fast Food
Number of seats
y= 0.9022x-23.56
Number of seats
y= 0.9022x-23.562
Traditional
Parking capacity
y =0.2454 x +71.23
Parking capacity
y =0.6315x +56.423
Parking capacity
y =0.8885x +44.423
Mixed
Parking capacity
y =0.2532 x +14.6
Gross Floor Area
y=0.1026x+9.452
Gross Floor Area
y =0.0716 x +2.550
The last step is to compare between the generated rates and other regional (Abu Dhabi Manual and
Dubai Manual) and international manuals (ITE Manual), as presented in Table 12.
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Table 12: Comparison between Proposed, Regional and International Rates
Land Use
Category
Amman Trip Rates
ITE Manual
Abu Dhabi Manual
Dubai Manual
AM
Noon
PM
AM
Noon
PM
AM
Noon
PM
AM
Noon
PM
Restaurants
0.889
0.675
0.958
9.94
7.49
14.13
0.03
0.35
0.45
0.29
6.93
8.12
Variable
Parking
capacity
Parking
capacity
Parking
capacity
1000 ft2
of GFA
1000 ft2
of GFA
1000 ft2
of GFA
Number
of seats
Number
of seats
Number
of seats
100 m2
of GFA
100 m2
of GFA
100 m2
of GFA
Fast Food
-
0.902
0.902
183.07
-
38.9
-
12.71
19.53
0
13.06
12.65
Variable
Not
available
Parking
capacity
Parking
capacity
1000 ft2
of GFA
Not
available
1000 ft2
of GFA
Not
available
100 m2
of GFA
100 m2
of GFA
100 m2
of GFA
100 m2
of GFA
100 m2
of GFA
5. Conclusions
Trip generation is the first step of the traditional four-step transportation planning process. It
explains the demand behaviour of traveling from a location to another, such as producing or
attracting trips by purpose. These step helps decision makers estimate generate zonal trips and
parking demand in order to guide future plans and developments.
In Amman, the capital of Jordan, the restaurants sector has grown rapidly over the past
decade. Restaurants in the city are major trip generators affecting the operating conditions of the
transportation system in their vicinity. This research studied 177 restaurants covering all licensed
restaurants categories, namely: international, traditional, tourist, fast food, and mixed. The first
questionnaire was used to determine the peak demand periods per meal for each restaurant type
(peak times and days). Friday morning (8:30-10:30 am) was found to be the peak day for breakfast,
Saturday afternoon (1:30-3:30 pm) for lunch, and Thursday evening (9-11 pm) for dinner.
The second questionnaire was used to collect data about the restaurants in Amman (type,
size, number of employees, and parking capacity). The traffic data collection involved manual
counts of the number of vehicles at each restaurant over the specific peak periods for each
restaurant category dictated by the first questionnaire.
To build the trip generation models, four independent variables were investigated: parking
capacity, gross floor area, number of seats, and number of employees. The analysis of the single
variable regression models showed that all four independent variables were significant factors
affecting the number of generated restaurant vehicle trips in Amman. The obtained trip generation
rates were compared to the available USA rates in the ITE trip general manual, as well as other
regional manuals. It was found that the local trip generation rates were generally different, either
higher or lower, and not comparable to any of the other three manuals. This dissimilarity could be
explained by the fact that travel behaviour as well as the socio-economic conditions and land use
characteristics are different. The results of this research provide a basic step towards developing a
trip generation manual for Jordan.
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... Based on studies discussed in Section 2, the number of pumps and GFA are always considered as independent variables for a petrol station trip generation model. Al-Madadhah and Imam [49] found that the number of seats is the best predictor for a fastfood restaurant. Hence, in this study, the dependent variable was the total number of inbound and outbound trips entering and leaving a petrol station, and the independent variables were number of pumps, GFA and number of restaurant seats. ...
... Based on studies discussed in Section 2, the number of pumps and GFA are always considered as independent variables for a petrol station trip generation model. Al-Madadhah and Imam [49] found that the number of seats is the best predictor for a fast-food restaurant. Hence, in this study, the dependent variable was the total number of inbound and outbound trips entering and leaving a petrol station, and the independent variables were number of pumps, GFA and number of restaurant seats. ...
A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant
Article
Full-text available
  • Nov 2021
A trip generation manual and database are important for transportation planners and engineers to forecast new trip generation for any new development. Nowadays, many petrol stations have fast-food restaurant outlets. However, this land use category has yet to be included in the Malaysian Trip Generation Manual. Therefore, this study attempted to develop a new trip generation model for the new category of “petrol station with convenience store and fast-food restaurant”. Significant factors influencing the trip generation were also determined. Manual vehicle counts at the selected sites were conducted for 3 h during morning, afternoon and evening peak hours. Regression analysis was used in this study to develop the model. A simple trip generation model based on the independent variable number of restaurant seats showed a greater value for the coefficient of determination, R2, compared with the independent variables gross floor area in thousand square feet and number of pumps. The multivariable trip generation model using three independent variables generated the highest R2 among all of the models but was still below a satisfactory level. Further study is needed to improve the model for this new land use category. We must ensure more accuracy in trip generation estimation for future planning and development.
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... The location and category of POIs were identified through OpenStreetMap and Google Maps, and the peak time of each POI was via Google popular times data. The POI category trip demand (d j ) was based on previous studies [23][24][25][26][27][28]. ...
Locating Method for Pick-up and Drop-off Spots for Shared Autonomous Vehicle-based Mobility Services
Article
  • Sep 2023
To provide safety to users and not disturb traffic flow, autonomous vehicles in shared on-demand mobility services cannot stop everywhere; thus, pick-up and drop-off (PUDO) spots must be dedicated or established for them. Our research objective is to propose a method to locate the PUDO spots for shared autonomous vehicle-based mobility services as this topic has been barely studied. The number of required PUDO spots is calculated, and the location is chosen considering the urban environment, walking radius, vehicle occupancy, and time for boarding and alighting among other parameters. Different from some methods applied to shared mobility, we consider the existing infrastructure (e.g., parking spaces) as potential locations for PUDO spots. The method is applied to a study case, demonstrating the applicability and providing the main findings: (i) the required number of PUDO spots decreases if willingness to walk increases; (ii) with a 3-min walking radius, 83% of curbside parking spaces can be repurposed, and 100% is reached with a 10-min walking radius; (iii) the minimum of 55% of curbside parking spaces can be repurposed with 10-min walking radius and without locating PUDO spots in private parking. Using our method, cities can determine the quantity of PUDO spots and their locations, being prepared in advance for the required changes in the existing infrastructure as well as the freed-up space to be repurposed.
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... Studies to improve movement through drive-throughs, with the goal of reducing waiting time (and therefore reducing emissions), do so from a developed country context (Whiting & Weckman, 2004;MarkDougherty, 1997). Studies conducted in developing countries related to fast-food drive-through facilities are limited to studies on trip generation (Al-Madadhah & Imam, 2020;Ahmed et al., 2014), and considerations of directing food selection from menus (Prasetyo et al., 2021). This paper is intended to serve as a starting point for important discussion on drive-through appropriateness. ...
Fast-food drive-throughs in developing countries: A modern convenience perpetuating unsustainable transport decisions? </i
Article
Full-text available
  • Jan 2022
Fast-food drive-throughs are a common feature in our fast-paced lives where convenience and service access are highly prized. This convenience comes at a high environmental price: long queues of idling vehicles guzzle fuel and generate emissions linked to global warming and health concerns. Drive throughs also generate significant income for franchisees and reduce parking requirements at fast-food outlets. Fast-food and drive-throughs are becoming more prevalent in the developing world at a time when these countries are facing rapidly increasing traffic congestion, driven by a desire for private transport use. This study considers aspects of sustainability of drive-throughs according to the mobility paradigm in the developing world, where vehicle centric urban form and rapid development are contributing to a mobility crisis. The drive-through is a prime example of capitalist vehicle-centric urban form. This paper is intended as a starting point for discussion on drive-through appropriateness in developing countries. Only two aspects of drive-through operations are considered in this initial investigation: the cost of using a drive through (fuel cost and environmental cost, approximated by the emissions load), and the space saving benefit realized by eliminating parking. This research found that drive-throughs save substantial land area by reducing parking and stimulate higher profits, making it unlikely that drive-throughs will be phased out, even with the significant environmental impact of drive throughs quantified in this paper. Recommendations for improved operations of drive-throughs are therefore suggested to mitigate long idling times in drive-through queues, and a call for more sustainable land-use planning is proposed.
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Estimation of New Development Trip Impacts through Trip Generation Rates for Major Land Uses in Palestine
Article
Full-text available
  • Oct 2018
Trip generation is a basic component of transportation planning and plays a key role in conducting Transportation Impact Studies. In Palestine, trip generation rates are not yet established and Transportation Impact Studies for new developments are not yet adopted. This research aims to establish local trip generation rates for major land uses. Field surveys were conducted in major cities in the West Bank, Palestine, and were based on international practices. A total of 138 sites comprising six major land uses were surveyed during the AM/PM peak periods using manual counting techniques. Trip generation rates were established for the studied land uses considering the most appropriate independent variable. The study results were compared with the respective regional and international rates; it was found that the local trip generation rates were generally different (higher, comparable, or lower). This can be attributed to the fact that travel behavior as well as socio-economic and land use characteristics, are different. The results of this research will form a basic step towards establishing proper transportation planning practices in Palestine and developing a Trip Generation Manual. The research recommends coordination with key stakeholders to promote the adoption of conducting Traffic Impact Studies at the policy level.
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Estimation of trip generation rates for residential areas in Jordan
Article
Full-text available
  • Jan 2018
The objective of this study was to develop trip generation for residential areas, where Irbid city was selected as a case study. Household survey was carried out to collect data on trips and their purposes on typical workdays and holidays. Socio-economic characteristics of households were also obtained through field interviews. A total sample of about 2500 households was interviewed. 100 households were interviewed for validation purposes. Both regression analysis and cross-classification approach were used to model trip generation rates. Analysis carried out in the study indicated that the number of generated trips is influenced by family size, car ownership and income level. On workdays, the analysis indicated that the number of home-based work trips constitutes about one-third of the total home-based trips. Also, it was found that the number of trips on holidays represent nearly one-third of the number of trips generated on workdays. Although performance of both approaches was very well, cross-classification approach proved to yield more accurate values. Compared with developed countries, trip generation rates for residential areas in Jordan were found to be substantially low.
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Analysis of Freight Trip Generation Model for Food and Beverage in Belo Horizonte (Brazil)
Article
Full-text available
  • Jan 2017
Today, one of the main challenges faced in urban logistics is the distribution of goods. In Brazil, mid to large cities have experienced negative consequences of unplanned urban sprawl as well as the lack of adequate transport infrastructure. As for the logistic standpoint, the relationship between urban planning and transportation planning must be better exploited towards the promotion of quality of life, and economic and environmental sustainability. The attractiveness of urban activities that attract the movement of people and goods and other component elements of urban space should be investigated. The presence of bars and restaurants falls within this context and is therefore vital and responsible for a significant percentage of jobs and revenue in a city. This paper presents the results of a freight trip generation model developed for pubs and restaurants in Belo Horizonte (Brazil). The data for the freight trip generation model were obtained by survey. A structured questionnaire was designed to obtain information about goods, frequency, operational time, place of performance of the loading/unloading of goods, establishment size and the number of employees. In creating the proposed models, a simple linear regression was applied to correlate the following variables: (i) number of trips versus area of the establishment; (ii) number of trips versus number of employees; (iii) number of trips versus operation day of the establishment. With the results of the linear regression for trip generations, data interpolation was conducted based on the standard deviation of the results to define the sample classification bands. Finally, the resulting trip generation surface was analysed together with other geographic data such as demographic data, road network density and socioeconomic data. Findings indicate the importance of a mathematic-geographic model for trip generation as a feasible approach to support transportation planning & operation for urban goods distribution. Critical information such as the high concentration of pubs and restaurants in the same region can reinforce the vocation of the city for trading. However, an elevated number of freight vehicles to meet a high and growing demand becomes a problem especially in areas where the urban road network is not ecient (not properly designed and parking spaces not properly used). This study also highlights the need for an urban freight mobility plan and public policies, by o↵ering sustainable alternatives for urban goods distribution, ⇤ The support of the National Council for Scientific and Technological Development (CNPq) is acknowledged and appreciated. The authors thank the Municipality of Belo Horizonte for the database, which allowed the analysis of this study. The authors would also like to thank anonymous reviewers for the suggestions to improve this paper. 17 18 L. K. Oliveira et al. which improve the urban environment. By using geospatial analysis, the study delivered statistics data and maps to catch the attention of decision makers and transportation managers, therefore facilitating the discussion on transportation policies in the city of Belo Horizonte.
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Trip Generation by Transportation Mode of Private School, Semi- private and Public. Case Study in Merida-Venezuela
Article
Full-text available
  • Dec 2016
The trip generation model (TGM) is the first step in transportation forecasting, this is useful for estimating travel demand because it can predict travel from or to a particular land use. Typically, the analysis focuses on residential trip generation as a function of the social and economic attributes of households, but nonresidential land use suggests others variables. Travel generator poles such as Private school, Semi-private, and Public; have not been studied in Venezuela. The TGMs shows by the Institute of Transportation Engineers (ITE), EE.UU, are typically used and could be not appropriate. By using stepwise regression and transformation of data, were found high correlation coefficients and substantial improvements in the variability of data from several schools. The trip generation rates (TGRs) by transportation mode: walking, motorcycle, public transport, and cars, can be compared and be included in the Ibero-American Network of travel attractor poles.
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Trip generation rates and land use - Transport planning in urban environment
Article
Full-text available
  • Jan 2001
In order to assess the land use policy implications, trip generation rates is essential knowledge for both planners and engineers working in the field of town planning and transportation. Within the framework of this paper trip generation rates are presented for the Thessaloniki Metropolitan Area (T.M.A). The structure of the land use system in T.M.A evidently produces many problems in the transportation system. The results of research activities of the Laboratory of Transportation Engineering of A.U.Th concerning the trip generation rates regarding different land uses (education, administration, banks, cultural, hotels industry, hospitals) in T.M.A are presented and discussed. These results are in the form of mathematical equations that have been derived with regression analysis and take into account data resulting from properly designed questionnaire surveys.
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A Comprehensive Study on Trip Attraction Rates of Shopping Centers in Dhanmondi Area
Article
Full-text available
  • Aug 2012
Studies of people trips and vehicular attraction to activity centers have been conducted in western countries but few have been done in Asian countries. In Bangladesh, this kind of study has been newly introduced. In this study, trip rate analysis method is used to estimate the trip attraction rates of the shopping centers at Mirpur Road, Dhanmondi area of Dhaka. As part of this study, a total of six shopping centers were surveyed, for which the number of vehicles (car) and persons entering the shopping center during peak hour for every 15 minutes time interval were counted. The approach deals with the relationship the trip attraction rates of the shopping center as a whole. It relates the trip attraction of the shopping center as a function of the physical features of the shopping center, e.g. total parking spaces, gross floor area, and the number of stores in the shopping center. It would be useful in the regional transportation network or the channelization of traffic control around a shopping center. The data would be useful in assessing the traffic impacts surrounding a new shopping center, and also the region wide traffic volume impacts.
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Trip-generation models for multiuse highway commercial developments
Article
  • Feb 1998
  • ITE J
Trip-generation characteristics for highway commercial developments that combine gasoline station/convenience store/fast-food restaurant are important in determining the potential traffic impact on adjacent roadways.
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Trip Generation at Fast Food Restaurants in Saudi Arabia
Article
  • Feb 2008
  • ITE J
This study developed trip generation rates at fast food restaurants in jeddah, saudi arabia. the results showed that vehicle trip rates were not significantly different when compared with respect to the type of food served, the presence of a drive-through window and origin. a comparative analysis showed that the rates mentioned in trip generation were much higher than those found in this study.
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