A study of influence zone for railway stations of different hierarchy in the Indian Railways: analyzing passenger travel patterns

Figures & data

Table 1. Quantitative assessment of influence zones for railway stations.

Factors for Influence zone	Quantitative assessment of influence zones for railway stations	Reference
Distance Based	3 km Radius - France	French Transport Firms (Govt. of France)
	5 km Radius – France	(Cervero, 1995)
	Distance of 7 km for stations – Canada	(Ermacora et al., 2013)
	80% of railway stations have an influence within 6 km in Europe	(Hasiak & Richer, 2012)
	6 km radius to 8.5 km radius	(Hasiak & Bodard, 2016)
	For Regional Rail Transit system o Core Station Area- 400 m o Primary Influence zone – 800 m o Secondary Influence zone – 8000 m	(Neff & Dickens, 2009)
	800 m Influence zone	(Ohland & Dittmar, 2004; Untermann, 1984)
	4500 m and 8700 m in Philadelphia and San Francisco, respectively	(Bradley J & Rivasplata, 2014)
	1.96 and 2.13 km for home-to-station and station-to-work trips for, respectively	(Zhao & Li, 2017)
Travel Time based	Driving accessibility in the range from 15 to 20 minutes around the station	(Hostis et al., 2010)
	40 min driving accessibility	(Hasiak & Bodard, 2016)
	5-min walk	(Bernick & Cervero, 1997)
	10-min Walk	(Calthorpe, 1993)

Figure 1. Sample size determination for Passenger OD surveys.

Figure 2. Comparison of passenger characteristics for Category 4 and Category 1 Stations.

Figure 3. Trip Length Frequency distribution for selected Category 4 Stations.

Table 2. Influence zones based on primary study.

Zone	Sonipat (Catchment in Km)	Karnal (Catchment in Km)	Kurukshetra (Catchment in Km)
Core Zone (25 Percentile)	1.6	0.8	1.8
Primary Zone (50 Percentile)	2.1	2.3	2.5
Secondary Zone (75 Percentile)	3.1	4	4.2

Figure 4. Trip Length Frequency distribution for selected 2 and 3 Stations.

Figure 5. Trip Length Frequency distribution for selected Category 2 station - Ambala.

Table 3. Observed Influence zone for Category 3 Stations.

Zone	Panipat (Catchment in Km)	Amritsar (Catchment in Km)
Core Zone (25 Percentile)	2.4	1.7
Primary Zone (50 Percentile)	3.2	4
Secondary Zone (75 Percentile)	3.4	4.3

Table 4. Observed Influence Zone for Category 2 Stations.

Zone	Ludhiana (Catchment in km)	Jalandhar (catchement in km)	Ambala (Catchment in km)
Core Zone (25 Percentile)	3.5	3.8	6.2
Primamry zone (50 Percentile)	6	5.7	7.4
Secondary Zone (75 Percentile)	7.1	6.8	7

Figure 6. Trip Length Frequency distribution for selected Category 1 station – New Delhi.

Figure 7. Sample size determination for Station selection.

Table 5. Influence zone assessment for different Hierarchy of stations.

Stations	Railway Station Category	Importance of the station (Bhatnagar & Ram, 2023)	Contour Catchment	Average Secondary Influence zone (Km)
New Delhi	1	0.83	18.5	16.9
Anand_Vihar	1	0.54	18.1
Old Delhi	1	0.73	17.8
Nizamuddin	1	0.54	16.8
Lucknow	1	0.55	13.2
Sarai_Rohilla	2	0.43	11.2	8.4
Ghaziabad	2	0.45	8.5
Ambala	2	0.53	7.7
Varanasi	2	0.46	7.6
Ludhiana	2	0.44	7.1
Jalandhar	3	0.36	6.8	3.90
Amritsar	3	0.4	4.3
Jammu_Tawi	3	0.35	4.25
Bathinda	3	0.35	3.85
Chandigarh	3	0.36	3.65
Bareilly	3	0.34	3.65
Patiala	3	0.37	3.5
Panipat	3	0.34	3.35
Haridwar	3	0.36	3.35
Moradabad	3	0.42	3.25
Saharanpur	3	0.39	2.9
Faridabad	4	0.27	3.5	3.1
Rohtak	4	0.33	3.49
Gurgaon	4	0.29	3.4
Muzaffarnagar	4	0.29	3.35
Kurukshetra	4	0.31	3.2
Sonipat	4	0.3	3.2
Meerut_Cantt	4	0.22	3.2
Raebareli	4	0.29	3.2
Rampur	4	0.25	3.15
Dehradun	4	0.25	3.1
Hardoi	4	0.3	2.9
Karnal	4	0.29	2.85
Amethi	4	0.23	2.85
Hapur	4	0.3	2.85
Meerut	4	0.33	2.8
Roorkee	4	0.27	2.75

Table 7. Pearson Correlation among Railway station Category, Importance of the station and Contour catchment.

Correlation matrix (Pearson):
Variables	Railway Station Category	Importance of the station	Contour Catchment
Railway Station Category	1	0.814	0.807
Importance of the station	0.814	1	0.785
Contour Catchment	0.807	0.785	1

Note: Values in bold are different from 0 with a significance level alpha=0.01.

Table 6. Summary Statistics for the selected stations.

Variable	Observations	Minimum	Maximum	Mean	Std. deviation
Railway Station Category	40.000	1.000	4.000	3.027	1.067
Importance of the station	40.000	0.220	0.830	0.380	0.131
Contour Catchment	40.000	2.750	18.500	5.923	4.843

Table 8. Summary Table for contour catchment.

Variable	Observations	Minimum	Maximum	Mean	Std. deviation
Contour Catchment	40	2.750	18.500	5.923	4.843

Table 9. Category-wise Contour Catchments (K-Mean Clustering).

Category of Stations	Contour Catchment in Km
Category 1	17.90
Category 2	12.20
Category 3	07.54
Category 4	03.35

Table 10. Anova Test for k-Mean Clustering.

	ANOVA
	Cluster		Error
	Mean Square	df	Mean Square	df	F	Sig.
Station Importance	.156	3	.005	33	34.081	.000
Contour Catchment	278.267	3	.286	33	972.700	.000

Figure 8. Railway Station Importance vs Contour Catchment for Indian Railway Stations.

Table 11. Summary table of regression analysis.

Regression Statistics
Multiple R	0.886
Adjusted R Square	0.779
Standard Error	2.275
Observations	40

Figure 9. Importance of the station vs Contour catchment.

Figure 10. Box-plot for Contour catchment and Railway Station Importance.

Figure 11. Indian Railways Influence zone mapping.

Cervero, R. (1995). Planned communities, self-containment and commuting: A cross-national perspective. Urban Studies, 32(7), 1135–1161. https://doi.org/10.1080/00420989550012618

 Web of Science ®Google Scholar

Ermacora, R., Morency, C., & Paez, A. (2013). Geodemographic analysis around rail stations to measure urban. S.L., WCTR conference.

 Google Scholar

Hasiak, S., & Richer, C. (2012). Des tramways contre-nature? Regards croisés sur les processus de décision des projets de TCSP de l’arc Sud de l’aire métropolitaine lilloise. Revue Géographique de l’Est, 52(1). https://doi.org/10.4000/rge.3547

 Google Scholar

Hasiak, S., & Bodard, G. (2016). Influence areas of railway stations: How can we explain their geographic forms?. WCRR.

 Google Scholar

Neff, J., & Dickens, M. (2009). 2009 public transportation fact book. American Public Transportation Association.

 Google Scholar

Ohland, G., & Dittmar, H. (2004). Best practices in transit-oriented development. s.l.: Washington Press.

 Google Scholar

Untermann, R. K. (1984). Accommodating the pedestrian: Adapting towns and neighbourhoods for walking and bicycling. TRB.

 Google Scholar

Bradley J, F., & Rivasplata, C. R. (2014). Public transit influence zones: The curious case of cycle-transit users. Transportation Research Record: Journal of the Transportation Research Board, 2419(1), 101–14. https://doi.org/10.3141/2419-10

 Google Scholar

Zhao, P. A. S. L., & Li, S. (2017). Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing. Transportation Research Part A: Policy & Practice, 99, 46–60. https://doi.org/10.1016/j.tra.2017.03.003

 Web of Science ®Google Scholar

Hostis, A. L., Wulfhorst, G., Morkisz, S., Pretsch, H., Stransky, V., Leysens, T. (2010). An urbanism oriented towards rail in Germany and France: Selected findings of the bahn.Ville project (12th ed.). WCTR.

 Google Scholar

Bernick, M., & Cervero, R. (1997). Transit villages in the 21st century. s.l.: TRB.

 Google Scholar

Calthorpe, P. (1993). The next American metropolis: Ecology, community, and the American dream. s.l.: Princeton architectural press.

 Google Scholar

Bhatnagar, R. V., & Ram, S. (2023). Classifying Indian Railway passenger stations using a multi-criteria framework. Transportation in Developing Economies, 9(3). https://doi.org/10.1007/s40890-022-00173-4

 Google Scholar

Data availability statement

Due to the nature of the research, supporting data is not available.