Spatial Modeling of Rural Travel Flow and Analysis of Factors Affecting Travel Demand Case Study: (Villages of Tabriz County)

Document Type : Original Research

Authors
M. Aghayari Hir 1
M. Zaheri 2
N. Rahimzadeh 3
1 Faculty Member of the Department of Geography and Rural Planning, University of Tabriz
2 Associate Professor of Geography and Rural Planning, Faculty of Geography and Environmental Planning, University of Tabriz
3 Ph. D. student in Geography and Rural Planning, University of Tabriz
10.50541/HSMSP.26.1.63
Abstract
Introduction

Lack of essential services increases the need for mobility and relocation in rural areas and makes people travel to meet their needs. The need for mobility along with the absence of public transportation services, while prolonging travels, has affected travel demand and flow, so that people use several vehicles in a travel chain (origin-destination) (Stfen and Hunt, 2006: 104). Various spatial factors affect the flow, destination and volume of trips, which can be shown by spatial analysis and modeling as one typical method to do this. Spatial statistics tools include a set of techniques and methods for describing and modeling spatial data. Use of spatial statistics helps us to increase the accuracy of results and observations in cases where the distribution or dispersion of data in space is complicated. In this paper, geographically-weighted regression and spatial modeling have been used to show the volume and travel flow in villages, and also the factors affecting them to determine, besides the origin and destination of rural travels, travel volume and their spatial distribution. The question is what factors are influential in the destination of these trips and their volume, and to what extent is the impact of each of these factors on the frequency of rural travel demands.

Methodology

The present article is a descriptive-analytical method with a practical purpose and the data is collected both in the field and from the existing documents. In the documentary section, some data were collected from 1390 and 1395 censuses and some other using questionnaires. The statistical population of the study were all rural households in Tabriz County, where a researcher-made questionnaire was used to obtain travel information. Since the number of households amounted to 33379 families, using the Cochran formula 320 sample sizes were selected and questionnaires were randomly distributed between rural households. Given that the primary purpose of this study is to model the spatial pattern of passenger flow in space and time, so that the spatio-temporal mobility of passengers in the road network can be seen, three components including the extraction of passenger travel pattern (Origin-destination), determination of the travel routes, and the factors affecting these trips were identified. In the first step, the data obtained from the questionnaire were processed to model and plot the travel pattern (origin-destination of trips). For this purpose, it was necessary to identify the villages with and without bus stations and public transportation servicesas well as the travel routes of passengers. The next step is modeling the travel patterns and flow of passengers. In this study, ArcGIS software was used to show the flow of travel. This mapshows the direction of movement and the volume of the flow adjusted by line widths. Using the simulated travel routes, a flow matrix was obtained from the travel volume among all villages, small towns, and Tabriz city. Then, to investigate the dominant pattern of the travel demand, spatial autocorrelation analysis (local Moran pattern) and hot spots were used to determine the pattern of space travel distribution. Finally, in order to reveal the most influential factor affecting travel demand as the independent variable, a geographically-weighted regression model was formed and the relationship between travel and such indicators as demographic, economic, and social factors were analyzed and modeled.

Results and discussion

Initially, the data collected from the villages were entered into the ArcGIS software to obtain the origins and destinations of the passengers, their travel directions, and the way their travels were distributed. Then, using Spatial analyst tools, the direction and spatial distribution of trips were obtained by a straight line as an Origion-Destination matrix for short-term trips to Tabriz city. The results showed that many trips were made outside the countryside to the city of Tabriz, and some of these trips were made to large villages and small towns. According to the data extracted from the questionnaire, many of these trips are made by car or taxi because many of these villages are on the roads where public transport does not pass through.

Hot spot analysis has been used to identify villages with high travel rates. The results obtained from the analysis of hot spots indicate that the main places of travels are mostly in the villages of Maidan Chai and Aji-Chay, which seems to be due to the access to public transportation services and the existence of villages with buses to Tabriz city in these rural areas. In the next step, in order to model and explain the effect of research variables on travel demand, GWR was used in GIS. According to the results of the analysis, the parameters of R2 and adjusted R2 are 0.8 and 0.75, respectively. This means that based on a geographically-weighted regression, the indicators considered at the 0.95 confidence level explain 0.80 of the trips and have acceptable accuracy in modeling the spatial relationships of the factors affecting the travel demands of individuals. In addition, the AICc value indicates a lower number, indicating a better fit of the observed data. Due to the different nature of the indicators, the effect of each of the indicators and factors on the amount of travels was investigated separately. The results show that the highest amount of R2 with a value of 0.72 is related to the access to the public transport, followed by the number of workers outside the village with a value of 0.70. Among these, the least influential factor is the total travel time to reach the destination with a value of 0.21, which indicates that people travel to access services and meet their needs regardless of the total time to reach their destinations.

Conclusion

The results of the study showed that several factors have affected the heterogeneity of travel demands in rural areas, which, according to the study, access to the public transport and people working outside the village have the most impact. In addition, most of daily trips to the city of Tabriz are done for business and by personal cars. These trips are based on hot spot analysis in Maidan Chai and Ajichay villages, where most of the villages near the city of Tabriz have access to the public transport (bus) and taxi. The short distance between these villages and the city of Tabriz, the existence of public transportation services in them, and consequently lower costs and ease of movement have caused the accommodation of the overflow of Tabriz in these villages and have increased the demand for travel in these villages, in such a way that during the day, the movement of travelers in these villages is the same as daily trips in the city of Tabriz, and educational, recreational, shopping, health, etc. trips are made from these villages to the city of Tabriz. In addition, the presence of several industrial towns and factories near the villages of Ajichai and Maidanchai has increased the number of business trips in these villages.

Keywords

Subjects

- آزادی قطار، سعید؛ ابوالفضل مشکینی، عبدالرضا رکن‌الدین افتخاری، احسان مصطفوی، محسن احدنژاد روشتی (1396). «تبیین رابطه‌ی بین قابلیت پیاده‌مداری شهری با توزیع فضایی مرگ و میرهای ناشی از سرطان‌های سینه و کورکتال در مناطق شهر تهران». نشریه برنامه‌ریزی و امایش فضا، دوره 21، شماره 3: 94-55.
- افندی‌زاده، شهریار؛ رحیمی، امیرمسعود (1388). «مدل تحلیل عاملی برای انتخاب عوامل مؤثر بر تقاضای سفر با اتوبوس-های بین شهری». پژوهشنامه حمل‌ونقل، سال هفتم، شماره اول، 10-1.
- سلطانی، علی؛ احمدیان، علیرضا؛ اسمعیلی ابوکی؛ یوسف (1399). «کاربرد مدل رگرسیون وزن دار فضایی (GWR) در بررسی روابط بین متغیرهای فضایی در یک پهنۀ شهری (نمونه موردی: منطقه 7 شهرداری تهران)». نشریۀ معماری و شهرسازی آرمان‌شهر، دوره 3، شماره 4، 110-99.
- شمشیری، سجاد، شهبازی، حبیب؛ تقی‌پور جاوی، شهاب‌الدین (1396). «تحلیل ارتباطات بین متغیرهای فضایی در دشت-خوان‌میرزا:مقایسه‌ی کارایی الگوی رگرسیون وزنی جغرافیایی و الگوی حداقل مربعات»، نشریه جغرافیا و توسعه، دوره 15، شماره 48، 112-95.
- شیرزادی بابکان، علی؛ مسگری، محمدسعدی (1390). «مدل‌سازی تقاضای سفر با استفاده از بلوک آماری: مطالعه موردی شهر تهران»، مدرس علوم انسانی- برنامه‌ریزی و آمایش فضا، شماره 1، پیاپی 71. 172-152.
- عباس‌پور، رحیم‌علی؛ بهاری، روح‌الامین؛ پهلوانی، پرهان (1394). «پهنه‌بندی آلودگی ذرات معلق با استفاده از مدل‌های آماری محلی در GIS (مطالعه موردی: شهر تهران)». نشریه علوم و فنون نقشه‌برداری، دوره 5، شماره 3، 173-165.
- عشورنژاد، غدیر؛ فرجی سبکبار، حسنعلی؛ امیراصلانی، فرشاد (1395). «مدل‌سازی روابط فضایی عوامل مؤثر در استقرار مراکز مالی و اعتباری موجود در شهر تهران با رگرسیون وزنی جغرافیایی»، پژوهش‌های جغرافیا و برنامه‌ریزی شهری، دوره 4، شماره 2، 240-223.
- علوی، سیدعلی، پرهیزکار، اکبر، رکن‌الدین افتخاری، عبدالرضا؛ قالیباف، محمدباقر؛ پورموسی، سیدموسی (1389). «مدل-سازی مکانی تقاضای سفر مبتنی بر روشی جدید برای پیش‌بینی و کاهش ترافیک (منطقه شش تهران)»، نشریه برنامه‌ریزی و آمایش فضا، دوره 15، شماره 4، 61-43.
- محرابیان، آزاده (1392). «بررسی عوامل مؤثر بر جابجایی مسافر در شبکه حمل‌ونقل ریلی در ایران». اقتصادی مالی، دوره 7، صفحه 197 -151.
- Abbaspour, Rahima Ali; Bahari, Ruholamin; Pahlavani, Parhan (2016), "Zoning of Particulate Matters (PM) Pollution Using Local Statistical Models in GIS (Case Study: Tehran Metropolisies)". Journal of Geomatics Science and Technology, 5 (3) :165-174, 2016
- Adom-Asamoah, G; Amoako, G; Adarkwa, K. K (2020), "Gender disparities in rural accessibility and mobility in Ghana", Cas studies on transport policy, 8, 49-58.
- Affendi Zadeh, Sh; Rahimi, A.M (2010),"Factor analysis model for selecting factors affecting travel demand by intercity buses". Transportation Reasearch, 7(22). 1-10.
- Alavi, S. A, Parhizkar, A, Rokn al-Din Eftekhari, A; Qalibaf, M. B; Pourmousi, S. M (2010)."Spatial modeling of travel demand based on a new method for forecasting and reducing traffic (Tehran 6 District)", Tarbiat Modares University Press-The Journal of Spatial Planning.15(4). 43-62.
- Ashournejad, Gh; Faraji Sabkbar, H. A; Amiraslani, F (2016),"Modeling the spatial relations in the factors effective on installation of current financial and credit institutes of Tehran using geographically weighted regression". Geographical urban planning research, 4(2): 223-240.
- AzadiQatar, S; Meshkini, A, Roknedin Eftekhari, A; Mostafavi, E; Ahadnejad Roshti, M (2017), "Explanation of relationship between urban walkability and death spatial distribution caused by colorectal and breast cancer", Tarbiat Modares University Press- The Journal of Spatial Planning, 21(3): 55-94.
- Clark, S; Watling, D (2003),"Modelling Network Travel Time Reliability Under Stochastic Demand Life-style and travel demand From Special Report 220: A Look Ahead: Year 2020", Transportation Research Board, National Research Council, Washington, DC, 1988, 149–189. Reproduced with permission of TRB.
- Chen, B, Cheng, X; Mei-PoKwan, TimSchwanen (2020)," Evaluating spatial accessibility to healthcare services under travel time uncertainty: A reliability-based floating catchment area approach". Journal of Transport Geography,87(21). 123-135
- Enoch, M., Potter, S., Parkhurst, G., Smith, M (2006),"Why do demand responsive transport systems fail?"Presented at the Transportation Research Board 85th Annual Meeting.
- Das, S., Dixon, K., Sun, X., Dutta, A., Zupancich, M (2017),"Trends in transportation research: Exploring content analysis in topics". Transp. Res. Rec. 2614 (1): 27–38
- Davison, L., Enoch, M., Ryley, T., Quddus, M.A., Wang, C (2012b),"Identifying potential market niches for demand responsive transport". Res. Transp. Bus. Manage. 3: 50–61.
- De Gruyter, C., Rose, G., Currie, G.(2015b), "Understanding travel plan effectiveness for new residential developments". Transp. Res. Record: J. Transp. Res. Board 2537, 126–136
- Dennett, A (2018),"Modelling population flows using spatial interaction models", Australian population studies, 2(2): 33-58.
- Gruyter.C, Geoff.R, Long, T, Truong. T, Wilson. K, Dukanovic, Z (2021), Exploring planning tribunal decisions on travel plans for new developments", Case Studies on Transport Policy,9: 51–58.
- Kutzbach, M.J (2009),"Motorization in developing countries: Causes, consequences, and effectiveness of policy options". J. Urban Econ. 65 (2): 154–166
- Juliu. S, Marquet, O, Miralles-Guasch, C (2017),"The influence of location, socioeconomics, and behaviour on travel-demand by car in metropolitan university campuses". Transportation Research Part D 53:149–160.
- Kerkman, K; Martens, K, Meurs, H (2018)"Predicting travel flows with spatially explicit aggregate models on the benefits of including spatial dependence in travel demand modeling". Transportation Research Part A 118: 68–88.
- Maghrifani, D Liu, F; Sneddon, J (2021), "Understanding Potential and Repeat Visitors’ Travel Intentions: The Roles of Travel Motivations, Destination Image, and Visitor Image Congruity". article first published online: June 17, Journal of transport research
- Mehrabian, A (2013), "Investigating the effective factors on passenger movement in the transportation and rail network in Iran". Financial Economics.7(22): 137-154
- Papangelis, K; Ashmore, F. H; R. Velga, N; Sripada, S (2018): "Exploring the rural passenger experience, information needs and decision making during public transport disruption", Transportation Business and Management, 6(11): 122-138.
- Porter, G., Hampshire, K., Abane, A., Tanle, A., Esia-Donkoh, K., Amoako, S., Agblorti, R., Owusu, S (2011)."Mobility, education and livelihood trajectories for young people in rural Ghana: a gender perspective". Children's Geogr. 9 (3–4): 395–410.
- Saroli, C; Lyon, C (2015), "Passenger transport in rural and sparsely populated areas in France". Imternational Transport Forum. Discussion Paper No. 2015-09: 222-242
- Sbista, SH; Justin, B. Hollander, Situ, M (2021), "A content analysis of transportation planning documents in Toronto and Montreal, a case study on transport policy", Case Studies on Transport Policy 9: 1–11.
- Shamshiri, S; Shahbazi, H; Tghipour Javi, S (2017),"Analyzing the Relations Between Spatial Variables in Khanmirza Plain: Comparison of Geological Weighted Regression and Ordinary Least Square Models". Geography and development Itanian Journal, 15(48): 95-112.
- Shirzadi Babakan, A; Mesgari, M. S (2010),"Modeling Travel Demand Using Statistical Block: A Case Study of Tehran"; Tarbiat Modares University Press- The Journal of Spatial Planning,15(1): 151-172.
- Soltani, A; Ahmadian, A; Ismaili Abuki; Y (2010),"GWR model application in investigation of spatial variables in an urban destrict: Case study of region 7", Municipality of Tehran, Journal of architecture, urban design and urban planning, 3(4), 99-110, 2010
- Sultana, Z; Mishraa, S, Christopher R. Cherry, Goliasa, M, Tabrizizadeh Jeffers, S (2018)," Modeling frequency of rural demand response transit trips". Transportation Research Part A 118: 494–505.
- Tao, S; Rohde, D, Corcoran, J (2014),"Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap". Journal of Transport Geography 41: 21–36
- Vitalea, M, Millwardb, H, Spinney, J (2019),"School siting and mode choices for school travel: Rural–urban contrasts in Halifax, Nova Scotia, Canada". Case Studies on Transport Policy 7, 64–72.
- Wang, C., Quddus, M., Enoch, M., Ryley, T., Davison, L (2014), "Multilevel modelling of demand responsive transport (DRT) trips in Greater Manchester based on areawide socio-economic data". Transportation 41: 589–610.
- Wang. X; Khattak, A (2012),"What Can Be Learned from Analyzing University Student Travel Demand?" Transportation Research Record 2322: 129-137
- Wang, C; Quddus, M; Enoch; M; Ryley, T; Davinson, L(2015),"Exploring the propensity to travel by demand responsive transport in rural area of Lincolnshire in England", Case studies on transport policy, 3, 129-136.
- Xing, Y; KeWang, J.Lu (2020), "Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China", Journal of transport geography. 102787.
- Yeates, S, Enoch, M (2013), "Travel plans from the developer perspective” Proceedings of the Institution of Civil Engineers: Urban Design and Planning, 166(9): 262-73.
- Yuan, J; Lin, H (2013), "An empirical study of commuting chracteristics in rural area", 13th COTA international conference of transportation professionals (CICTP 2013). Social and behavioral sciences 96: 114-122
- Zhang, Lei., Levinson, D (2018),"Agent- Based approach to travel demand modeling. Online available: Transportation research record": Journal of the transportation research board. 28-36.
The Journal of Spatial Planning and Geomatics
Volume 26, Issue 1
May 2022
Pages 63-88