Develop Trip Generation Model for Alexandria City - Egypt

Transcription

1 Develop Trip Generation Model for Alexandria City - Egypt AHMED ELKAFOURY 1,*, ABDELAZIM M. NEGM 1, MOHAMED H. ALY 2, MAHMOUD F. BADY 3 1 Environmental Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), P.O. Box 179, New Borg Al-Arab City, Alexandria, Egypt. 2 Transportation Engineering Department, Faculty of Engineering, University of Alexandria, Alexandria, Egypt. 3 Energy Resources Engineering Department,, Egypt-Japan University of Science and Technology (E-JUST), P.O. Box 179, New Borg Al-Arab City, Alexandria, Egypt. * Corresponding author: ahmed.elkafoury@ejust.edu.eg Abstract This paper tends to introduce a trip generation model for Alexandria city-egypt. This model can be implemented in the transportation planning process of the city since the city suffers from harsh transportation and travel problems. The model relates daily exchanged numbers (Q and Z) of trips at different Transportation Analysis Zone (TAZs) to its socio-economic data. Analysis of socio-economic and demographic data of different TAZs has been performed. Zonal data has been involved in Multiple Linear Regression (MLR) technique. Investigate attributes affect each of trip generation and trip attraction, the significant level of individual socio-economic variables for Q and Z has been statistically evaluated. Statistical indicators have been used to assess and verify the performance of the developed trip generation models. The model shows good performance since the models introduced acceptable CR values of 0.55 for Q model and higher value of 0.72 for Z model, NMSE 0.39 and 0.11 for Q and Z models respectively, and MG of 0.73 and 0.85 respectively. Key-Words Alexandria, Socio-economic data, Transportation planning, Trip generation model, Multiple Linear Regression (MLR) 1. Introduction Thanks to the rapid increase in population numbers and associated economic growth, the travel demand in urban areas is explicitly increasing. Under the umbrella of sustainable and environmental urban development planning, the role of the transportation planner is to manage and introduce different transportation policies to give urban authorities the tool to cape with this increase in travel demand. This is performed through modeling the transportation behavior and the transportation system to infer the problem key issues. And consequently, proposing different tested alternatives to relieve these problems In the four-stage prediction method, trip generation predictions are the first step of the traffic demand prediction process in the traditional four stage prediction method. It predicts the number of trips originating in or destined for a particular traffic analysis zone. Every trip has two ends - trips origin zone and trips destine zone [1]. The reliability of forecasting results influences the following steps such as trip distribution, mode split, and traffic assignment. Therefore, improved trip generation models are needed to improve forecasting precision [2]. From the transport demand perspective, the users most relevant characteristic is their socioeconomic level [3]. One of the widely used of these methods makes use of regression analysis using data collected in travel survey. The number of daily trips is a function of set of independent variables. This method can be applied to any situation since the data is regression analysis provided [4]. Alexandria city, second largest city in Egypt after Cairo, with its 32 km along the coast of Mediterranean, it holds two major Egyptian seaports named Alexandria seaport and Dekhela seaport which handles about 80% of the Egyptian import and export movements. It has suffered from severe transportation problems. The review showed little number of research papers and studies related to transportation system analysis of the city. The objective of this research is to develop a trip generation (production / attraction) model for daily trips in Alexandria city. First, the zoning system of the city has been introduced. On Transportation Analysis Zone (TAZ) level, socio-economic, demographic, and trip interchange data has been canalized. Multiple linear Regression (MLR) technique has been incorporated to develop the trip generation models. Finally, performance of the models has been statistically analyzed. This model helps mainly in the urban transportation planning process of the city. 2. Alexandria city transportation zoning system To analyze the transportation and traffic situation in the study area, there should be a proper splitting of the area into traffic analysis zones (TAZ). TAZs are the way to inventories the socio-economic, transportation systems, and traffic-related data of the study area on a geographical scale, aiming to ISBN:

2 divide a large set of region information into a number of spatially contiguous regions [5]. For Alexandria city as the study area in this research, the system invoked coincide with the administrative division (zones) of Alexandria city. This means that there are 13 TAZs for Alexandria city. They are, Monatza, Raml, Sidi Gaber, Moharam Bek, Bab Sharq, Atarin, Manshia, Gomrok, Laban, Mina Basal, Karmoz, Dekhela, and Amria. The selection of this transportation zoning system is to ensure accurate data providence since the socio-economic and travel demand data provided is at the nexus of the administrative division scale. 3. Analysis of socio-economic and demographic data This section analysis the socio-economic characteristics for Alexandria city's 13 TAZs in year 2006 Based on data provided from [6], [7], [8], [9], [10], [11]. 3.1 Population In year 2006, Alexandria city holds about 4 million inhabitants representing 5.6% of total population number of Egypt. the analysis of population characteristics in Alexandria city zones (Table 2) indicates that the most populous zone is Monatza which holds 1,173,803 inhabitants representing % of total population in the city. In the second rank, Raml zone holds 19% of the total city population (752,371 inhabitants). The least number of populations among all zones exists in Manshia which holds only 1% of the city population (23,616 inhabitants). This can be explained as this zone is a trading zone and did not hold many residential buildings. As an indicator, high population density areas increase the cost of urban transport systems, but for example less for the implementation of rail system than other modes [12]. A look on the population density (person/km2) all over the city zones of the zones indicates relatively high gross population density (The number of people inhabiting an urban area / total area of urban land) and residential population density (The number of people living in an urban area divided by the area of residential land). For the whole city, the average gross population density and residential population density are 25,706 (persons/km2) and 125,644 (persons/km2) in order. Old zones including Moharam Bek, Manshia, Karmoz, Laban are indicated as high density population zones as these are the origin place of Alexandrian inhabitants of the city. All over Alexandria city, the average annual percentage increase in population numbers is 1.6%. This rate is less than the yearly national rate of increase in population in the same time period which is 2.05%. 3.2 Age Characteristics The trip production rates distinguish for different age categories. For Alexandria city, the majority of the population (42%) in year 2006 is less than 15 years old. The second dominating age category in the city is the population who has ages between 35 to 60 years old. Former categories properly have an explicit influence on the daily trip production rates. This is because those two categories represent the student and the employees respectively. As shown in Fig. 1, on the districts scale, a reflection of the same age categories trends for the whole city is noticed. Only Amria district breaks this role as only 16% of its population is between 35 to 60 years old and about 17% of population is between 25 to 35 years old. This can be accounted for that this district is an industrial and hand working area in which these age categories are the power for that type of work. 3.3 Income Levels The investigation of income level data of population reflects its implication on the quality of life. Usually, as the income level increases, the trip production rates increase in the sequel. Old TAZs of Alexandria which have high density population including Moharam Bek, Manshia, Karmoz, Laban besides Mina Basal zones, are the settle of high proportion of low income households. About 35%, 29%, 64%, 35%, and 40% of population in those zones respectively represents low income satisfaction. The biggest portion of population among all Alexandria city zones that reported high income level is in Sidi Gaber. In which about 81% of population have high income level. Also, big portion of families reside the low population densities zones (Bab Sharq, Dekhela, and Monatza) reports high income level. See Fig Illiterate levels The illiterate levels in Alexandria city districts increases in western districts than eastern districts. This is clear in Figure 5 which illustrates the illiterate levels in population with ages between 15 to 45 years old in different districts for year This reveals the relation between portions of population of low income level in the district and the illiterate levels. Fig. 1 Distribution of population of different districts among different age groups in year In Montaza districts, which contain only 7.42% of the population with low income level, the illiterate level is the smallest among all districts. As the low income portion of population increases in western districts, the illiterate level increases to reach its peak in Amria district. In which, the illiterate level is about 22% of population with ages between 15 to 45 years. See Fig. 3. ISBN:

3 3.5 Employment In order to analyze the employment data, the total number of employees is different zones has been projected based on employment data of year 1996 and year 2002 mentioned in. From which, an average annual growth rate of employment has been derived for different zones and has found to be of 2.2% per year. Based on this rate, the inventory data of employment for different zones in year 2006 have been estimated and results are shown in Table I. Montaza zone holds the hugest employment power with employees in year This is suitable for this zone which holds the highest population number among all zones. The second place is occupied by the holder of second highest population number ( Raml zone) which holds employees. The smallest number of employees is recorded in Laban zone with only employees. Total number of employees in Alexandria city in year 2006 is Car ownership The analysis of private cars numbers in the Alexandria city illustrates the following facts: 1) Total private cars number in Alexandria city in year 2006 is , 2) The number of private car increase yearly by a rate of 6.5% yearly, 3) Car ownership is about 84 car/1000 inhabitant, and 4) Car ownership per 1000 inhabitants increases by a rate of 4.1% yearly. Fig. 2 Distribution of population of Alexandria city TAZs among different income level groups in year Fig. 3 Illiterate level in different districts of Alexandria city in year Land and Building Value Dekhela TAZ has the highest land value among all zones. The land value in it is 15 thousand Egyptian pounds per square meter (L.E/m2). At the nexus, the value of the square meter of buildings is relatively high with a 3250 (L.E/m2) of building. Sidi Gaber district have the second highest land value with 8500 (L.E/m2), but it holds the highest building value in the city with 4000 (L.E/m2). Old zone in the city which holds high population numbers and high population densities, including Manshia, Laban, and Moharam Bek besides Gomrok zone, have a relatively convergent land values. Mina Basal zone signed the lowest land value and building value of 600(L.E/m2), and 400 (L.E/m2) in order. Land and building values for different zones in illustrated in Fig Use of dwelling According to the 2006 Population and Housing Census, most of the dwellings of Alexandria are used for residential purposes. In all zones, residential usage of dwellings shares the biggest percentage among all usage categories. The usage of buildings as for work shares the least percentage in all zones except in Atarin and Manshia zones. In these zones, using building for work purposes shares 35,3% and 32,6% of the total buildings number. Distribution of using purposes of buildings in different zones is shown in Fig Land uses Alexandria city is extended for 32 km parallel to the sea with a narrow costal stretch of a width ranges between 1 to 5 kilometers. As illustrated in Figure 8, the prevailing land use purpose of the urbanized land all over the zones of Alexandria is the residential purpose. It shares the same percentage in Montaza, Raml, and Dekhela zones with about 82% of the zones areas. In all zones, except Montaza, Bab Sharq, and Gomrok, the second dominating land use purpose is the Economical land use. The second prevailing land use in Montaza zone is the Educational land use purpose. This is at the nexus of the educational situation analysis mention before which indicated Montaza as obtaining the highest number of educational places in any district. Utilities occupy the lowest land use purpose in almost all zones. Other services including health, religious, roads, and open areas; which are ISBN:

4 indicated in Fig. 6 as (Others) represent high contribution land use (33%) in 2) High population number in Montana zone (Highest among all TAZs). Fig. 5 Use of dwelling of different TAZs of Alexandria city. Fig. 4 Land and building value of different TAZs of Alexandria city in year 2006 Gomrok zone, while it shows lower contribution in other zones. 5. Travel demand and trip characteristics The Origin-Destination (O/D) matrix describes the travel demand commuted between pairs of transportation zones in the study area. As illustrated in Table 7, in year 2006, the total daily number of trips exchange between all transportation zones in Alexandria city is trip/day. From which, 40% are inter-zonal trips and 60% are exchange trips between pairs of zones. Analysis indicates that the maximum number of exchanged trips is trip/day. This travel demand is generated from Montaza zone to Raml zone. This refers to: 1) The higher attractively between the two zones due to the short travel distance between them. TAZ montazah Raml Sidi Gaber Fig. 6 Land use breakdown of different TAZs of Alexandria city. Fig. 6 Land use breakdown of different TAZs of Alexandria city in year ) The huge number of employees in Montana zone (First TABLE 1. ORIGIN DESTINATION MATRIX OF ALEXANDRIA CITY IN YEAR 2006 Bab Sharq Moharam Bek Atarin Manshia Gomrok Laban Karmoz Mina Basal Dekhela Amria Total trip production (Q) montazah Raml Sidi Gaber Bab Sharq Moharam Bek Atarin Manshia Gomrok Laban Karmoz Mina Basal Dekhela Amria Total trip attracted (Z) ISBN:

5 rank in number of employees among all zones) 4) High portion of population (about 46% of population) with 5) High income level which means more trip production rates. 6) The higher economic activity land use in Raml zone (About 11% urban land). For more clarification, Raml zone locates in Sidi Gaber district which holds main train station in the city represents the port to travel to other cities from Alexandria city. Big number of faculties of University of Alexandria is located in Sidi Gaber district. This represents attractiveness for faculty students from high income level in Montana zone. On the other hand, it is interesting to shed light upon the highest values of total trip production (Q) and trip attraction (Z) in the city O/D matrix. Raml zone recorded the first rank in total trip production and inter-onal trips with and trip/day respectively. This coincides with the big number of population in the zone which is an incentive for trip production. Also, the zone holds the second biggest number of employees among all zones. This brings forward more daily home-based work trips. Besides, the high value of land in this zone is amenable for more flee to zones with lower value of land for work. This is also clear from the land use purpose distribution of Raml zone. In which, about 82% are for residential purpose and only 13% for educational and economical activities which advocate more trips towards other zones for economic and educational purposes. Amria zone lies in the last rank regarding total trip production and interzonal trips with only 4035 and 2250 trip/day respectively. Regarding the trip attraction, Bab Sahrq zone occupies the first rank among all zones with attracting trip/day. This is ascribed to the low value of land and buildings in this zone which is suitable for commercial and economical activities. Latter reason is drawn from the land uses in this zones which have about 6.5% of land is for entertainment land use. This is the second highest percentage of land use for entertainment purpose among all zones of the city. Amria also is recorded in the last rank of trips attracted with only trip/day. With concern to trip characteristics, statistics indicates that 45% of total trip number is a return home trip. While about 23% and 16% of the trips are for the purpose of going to work and going to school. This falls in line with the small student to population ratio in the city which is 21%. Recreational and private affairs trips represent only 16% of all trip purposes. The reason refers to overall low income level in the city and also weakness of public transport system. 6. Trip generation model for Alexandria city To developed trip generation models: trip production and attraction models for the study area thirteen TAZs, zonal data has been involved in (MLR) technique. The socio-economic phenomena of different TAZ have been correlated to total number of trips (Q and Z) of zones. Initially, to investigate attributes affect each of trip generation and trip attraction, the significant level of individual socio-economic variables for Q and Z has been statistically evaluated to minimize sources of uncertainty in the developed models. This is obtained by estimating both the probability significant value (P-value) and linear coefficient of determination (R 2 ) between individual variables and Q and Z as dependent variables and every socioeconomic variable as explanatory variables. The explanatory variable is considered significant if it has P-value less than or equal to the significant level of 0.05 and have high linear coefficient of determination with comparison to other variables. This means that changes in the socio-economic variable has significant effect on changing the dependent variable (Q or Z). As shown in Table 2, population number (X1), employment number (X2), and percentage illiterates of population (X3) have been found to obtain the highest linear coefficient of determination (R 2 ) with the number of trip production of TAZs. They have R 2 of 0.31, 0.31, and 0.40 respectively. So, the significance level of each have been checked, and have been found significant variables in the trip production in TAZs as they have P-values of 0.047, 0.044, and respectively. For trip attraction, the socio-economic variables that have the highest linear coefficient of determination (R2) with the number of trip attracted to TAZs are the employment number (X2), percentage illiterates of population (X3) in addition to percentage of population with age between years old (X4), and percentage of land used for educational purpose (X5). Consequently, the significance level have been checked for those variables. Results indicated significant assurance as they have significant P-values of 0.050, 0.002, 0.050, and respectively. Regression work has been operated on the significant variables and number of trips produced and attracted to individual transportation zones. The results are two multiple regression models for both Q and Z as expressed in the following equations: here: (R 2 = 0.50) (1) (R 2 = 0.71) (2) Qi : is the number of trips produced from the TAZ I (trip/day), Zi : is the number of trips attracted to the TAZ I (trip/day), X1i : is the population number in TAZ i, X2i : is the employment number in TAZ i, X3i : is the percentage illiterates of population in TAZ i, X4i : is the percentage of population with age between years old in TAZ i, and X5i : is the percentage of land used for Educational purpose in TAZ i. The analysis of the proposed trip generation models introduces the following facts: ISBN:

6 1) The model coefficients of the socio-economic variables have signs match with the individual correlation Indication of the variable in the model Indication of the variable in the model For trip production model (Q model) P-value linear coefficient of determination (R2) between individual variables and Q Individual correlation coefficient between ) between individual variables and Q X X X For trip Attraction model (Z model) P-value linear coefficient of determination (R2) between individual variables and Z Individual correlation coefficient between ) between individual variables and Z X X X X coefficient of the variable in Table 8 which also match the engineering judgment. This means that the effect of the explanatory variable in the model is the same effect of the individual relation between the explanatory variable and the dependent variable (Q or Z). For example, the model coefficients of percentage illiterates of population in TAZ have negative signs in both Q and Z models ( and respectively). This is at the nexus with the negative sign of individual correlation coefficient between the percentage illiterates of population with Q and Z ( and respectively). 2) The percentage illiterate of population (X3i) has high effect on trip production (Q) since it has the highest absolute model coefficients value ( ). The negative sign of the coefficient illustrates reduction in trip production of the TAZ with the increase of percentage illiterate of population in the TAZ. The variable that has the smallest effect of trip production number is the population number (X1i) of TAZ which has a model coefficient of only ) The percentage of population with age between years old (X4i) has the highest effect on trip attraction of TAZ. It has the biggest absolute model coefficients value of ( ). The negative sign of the model coefficient indicates in trip attraction to the TAZ with the increase of percentage of population with age between years old in the zone. The employment number in TAZ (X2i) has inferior effect on trip attraction since it has the smallest model coefficient among all descriptive variable in trip attraction model. TABLE 2. SIGNIFICANT SOCIO-ECONOMIC VARIABLES FOR TRIP PRODUCTION (Q) AND TRIP ATTRACTION (Z) IN ALEXANDRIA CITY 7. Statistical assessment of developed trip generation model In this step, statistical indicators have been used to assess and verify the performance of the developed trip generation models for Alexandria city. This is to ensure the significance and agreeability of output values of the developed trip generation models with the dataset on which the modeling process have been based. The goodness of fit for each model is examined based on the values of the O-D matrix balance error, correlation coefficient of determination (R2) between modeled and actual base year trip production and attraction values, difference between average monitored and actual average of trip production and attraction values at the base year, Normalized Mean Square Error (NMSE), Frictional Biases (FB), and Geometrical Mean (MG), and Coincidence Ratio (CR). Calculated Values of the statistical indicators for both models are illustrated in Table 3. Analysis of results indicates the followings: 1) The O-D matrix balance error (which is the percentage difference between the sum of total number of trips produced and the sum of total number of trips attracted of all TAZs) indicates the quality of Q and Z models accompanied which forms the base for the trip distribution step of the 4 steps transportation planning process. The ideal situation is when the sum of trip generated and the sum of trips attracted in the O-D matrix is equalized. i.e. O-D balance error is zero and no calibration is needed. For the developed Q and Z models for Alexandria city, the balance error in is only 5.8%. 2) Percentage relative average error (Percentage difference between average actual trip numbers and average modeled trip numbers) is relatively small for both Q model and Z model representing percentages of % and 5.972% for both models respectively. Such values of errors may be neglected as we deal with numbers of trip thousands trips produced and attracted to transportation zones per day, 3) The NMSE which reflects the overall deviations between modeled and actual trip numbers for both models investigated equals 0.39 and 0.11 for Q and Z models respectively. Therefore, the performance of the model is considered acceptable and correctly describes the processes since NMSE is less than 0.5 [13], [14], 4) FB value for trip production and trip attraction model are and respectively. Such values are indicating small overall overestimation of trip numbers for Q model and small overall underestimation of trip numbers for Z model. Nevertheless, for both models, the FB values are acceptable since FB values lays between -0.7 and 0.7 ISBN:

7 [14], 5) MG values for Q and Z models respectively are 0.73 and 0.85 respectively. These values deviate from the ideal value of MG (1.00) [15], [16] with only 0.27 and 0.15 for both models respectively. Such figures of MG indicate small systematic errors and minute relative biases for predicted production and attraction TAZ s trip numbers with comparison to actual trip numbers, Statistical indicators Trip production model (Q) Trip Attraction model (Z) Mean error (trip/day) (trip/day) % Relative average error FB NMSE MG CR (R2) between actual and modeled trip numbers O-D matrix balance error 5.8% TABLE 3. STATISTICAL INDICATORS OF THE DEVELOPED TRIP GENERATION MODEL 6) CR compares the frequency distributions of estimated and observed trip numbers. The ratio measures the analogous pattern between the two distribution curves by estimating percent of area that coincides [17]. CR lies between 0 and 1.0, where a ratio of 1.0 indicates identical distributions. For Alexandria city, developed trip generation models introduced acceptable CR values of 0.55 for Q model and higher value of 0.72 for Z model, 7) The relation between monitored and modeled trip generation numbers has a correlation coefficient of determination R 2 = 0.57 and 0.72 for both Q and Z models respectively. 8. Conclusion A trip generation model describes the daily trip production and attraction (Q and Z consequently) interchange between different TAZs in Alexandria city - Egypt has been produced. Based on socio-economic, demographic, travel demand data in year 2006, after analysis of collected dated, this model related the trip generation behavior in the city to significant parameters. The significance level has been statistically determined based on the probability significant value (P-value) and linear coefficient of determination (R2) between individual variables and Q and Z as dependent variables and every socio-economic variable as explanatory variables. Regression work has been operated on the significant variables and number of trips produced and attracted to individual transportation zones. The results are two multiple regression models for both Q and Z. statistical indicators have been used to assess and verify the performance of the developed trip generation models for Alexandria city. The O-D matrix balance error is only 5.8%. Percentage relative average error is relatively small for both Q model and Z model representing percentages of % and 5.972% for both models respectively. The NMSE equals 0.39 and 0.11 for Q and Z models respectively. FB value for trip production and trip attraction model are and respectively. Acceptable CR values of 0.55 for Q model and higher value of 0.72 for Z model. Therefore, the performance of the model is considered acceptable. This model helps mainly in the urban transportation planning process of the city. ACKNOWLEDGMENT The first author would like to thank Egyptian Ministry of Higher Education (MoHE) for providing him the financial support (PhD scholarship) for this research, as well as Egypt Japan University of Science and Technology (E-JUST) for offering the facility and tools needed to conduct this work. REFERENCES [1] A.. Atoyebi, T.. Gbadamosi, I. I.. Nwokoro, and F.. Omole, Analysis of Intra- City Public Transport System of Ojuelegba Park, Lagos State, Nigeria, Mediterr. J. Soc. Sci., vol. 6, no. 2, pp , [2] T. F. Golob, A simultaneous model of household activity participation and trip chain generation, Transp. Res. Part B Methodol., vol. 34, no. 5, pp , [3] A. a. Amavi, J. P. Romero, A. Dominguez, L. dell Olio, and A. Ibeas, Advanced Trip Generation/Attraction Models, Procedia - Soc. Behav. Sci., vol. 160, no. Cit, pp , [4] S. Goel, Artificial Neural Network Based Model for Traffic Production and Attraction : A Case Study of All the Zones of Delhi Urban Area, pp , [5] L. Wang, J. Tang, X. Fei, and M. Gong, A mixed integer programming formulation and solution for traffic analysis zone delineation considering zone amount decision, Inf. Sci. (Ny)., vol. 280, no. May, pp , [6] M. M. Abdo, H. a Ayad, and D. Taha, % reviewed paper The Open Cities Approach: a Prospect for Improving the Quality of Life in Alexandria City, Egypt Mai M.Abdo, Hany A.Ayad, Dina Taha, vol. 0, no. May, pp , [7] M. M. M. Abdel-Aal, Calibrating a trip distribution gravity model stratified by the trip ISBN:

8 purposes for the city of Alexandria, Alexandria Eng. J., vol. 53, no. 3, pp , [8] Central Agency for Public Mobilization Statistics CAPMAS, Yearly Statistics Report, [9] P. G. O. F. P. (GOPP), The General Plan for the City of Alexandria, Cairo, Egypt, [10] Central Agency for Public Mobilization Statistics CAPMAS, Yearly Statistical Book, [11] M. of Housing, National Urban Observatory, [12] R. Cervero and E. E. Guerra, Urban Densities and Transit : A Multi-dimensional Perspective, no. September, p. 15, [13] G. Raducan and I. Stefanescu, A Qualitative Study of Air Pollutants from Road Traffic [14] D. Bhawna, A. Pal, and G. Singh, Assessment of Vehicular Pollution In Dhanbad City Using Caline 4 Model, Int. J. Geol. Earth, [15] A. kafoury, A. M. Negm, M. H. Aly, M. F. Bady, and T. Ichimura, Develop dynamic model for predicting traffic CO emissions in urban areas, Environ. Sci. Pollut. Res., [16] A. kafoury, A. M. Negm, M. F. Bady, and M. H. Aly, Modeling Vehicular CO Emissions for Time Headway-based Environmental Traffic Management System, Procedia Technol., vol. 19, pp , [17] J. S. Chang, D. Jung, J. Kim, and T. Kang, Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area, Transp. Lett., vol. 6, no. 2, pp , ISBN: