-افندی زاده, شهریار، احمدی نژاد، محمود، کلانتری، نوید و نجفی نژاد، علی رضا (1400). مدلسازی یکپارچه حملونقل و کاربری زمین با استفاده از روشهای شبیهسازی (مطالعه موردی: شهر قم). فصلنامه علمی پژوهشنامه حمل و نقل، 18(1)، 112-95. doi:10.22034/TRI.2021.118089
-آمارنامه شهر مشهد. (1398). معاونت برنامه ریزی و توسعه سرمایه انسانی شهرداری مشهد.
-زارعی، حمید و ممدوحی، امیر رضا (1400). ارزیابی کمی عدالت در حمل و نقل همگانی چندوسیله ای شهری شامل مترو، اتوبوس و BRT (مطالعه موردی: کلان شهر تهران). فصلنامه علمی پژوهشنامه حمل و نقل، 18(2)، 58-45. doi:10.22034/TRI.2021.84259
-زرین مهر، امیرعلی و ملوک زاده، هانیه (1402). طراحی شبکه خطوط همگانی با استفاده از اولویتبندی حریصانه خطوط در شبکههای شطرنجی. فصلنامه علمی پژوهشنامه حمل و نقل، 20(2)، 160-147. doi:10.22034/TRI.2022.318126.2988
-سرشماری عمومی نفوس و مسکن. (1395).
-Alsger, A., Assemi, B., Mesbah, M., & Ferreira, L. (2016). Validating and improving public transport origin–destination estimation algorithm using smart card fare data. Transportation Research Part C, 68, 490-506. doi:10.1016/j.trc.2016.05.004
-Alsger, A., Mesbah, M., Ferreira, L., & Safi, H. (2015). Use of Smart Card Fare Data to Estimate Public Transport Origin–Destination Matrix. Transportation Research Record Journal of the Transportation Research Board, 2535(2535), 88-96. doi:10.3141/2535-10
-Alsger, A., Tavassoli, A., Mesbah, M., Ferreira, L., & Hickman, M. (2018). Public transport trip purpose inference using smart card fare data. Transportation Research Part C, 87, 123-137. doi:10.1016/j.trc.2017.12.016
-Assemi, B., Alsger, A., Moghaddam, M., Hickman, M., & Mesbah, M. (2020). Improving alighting stop inference accuracy in the trip chaining method using neural networks. Public Transport, 12, 89-121.
doi:10.1007/s12469-019-00218-9
-Atsalakis, G. (2014). New technology product demand forecasting using a fuzzy inference system. Operational Research an International Journal, 14, 225-236. doi:10.1007/s12351-014-0160-y
-Baek, J., & Sohn, K. (2016). Deep-Learning Architectures to Forecast Bus Ridership at the Stop and Stop-To-Stop Levels for Dense and Crowded Bus Networks. Applied Artificial Intelligence, 30(9), 861-885.
doi:10.1080/08839514.2016.1277291
-Barbosa, H., Barthelemy, M., Ghoshal, G., James, C., Lenormand, M., Louail, T., Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports. doi:10.1016/j.physrep.2018.01.001
-Barry, J., Freimer, R., & Slavin, H. (2009). Use of Entry-Only Automatic Fare Collection Data to Estimate Linked Transit Trips in New York City. Transportation Research Record Journal of the Transportation Research Board, 2112(2112), 53-61. doi:10.3141/2112-07
-Barry, J., Newhouser, R., Rahbee, A., & Sayeda, S. (2002). Origin and Destination Estimation in New York City with Automated Fare System Data. Transportation Research Record: Journal of the Transportation Research Board, 1817(1), 183-187. doi:10.3141/1817-24
-Bonnel, P. (2021). Benefits of cellular telecommunication and smart card data for travel behavior analysis. International Transport Forum Discussion Paper, No. 2021/06. doi:10.1787/3884255b-en
-Bouteiller, C., & d’Arcier, B. F. (2015). How fare simulation tools in urban public transport can benefit from smart card data analysis? Retrieved from http://web.mit.edu/cron/project/CUPUM2015/proceedings/Content/pss/199_bouteiller_h.pdf.
Briand, A. S., Côme, E., Trépanier, M., & Oukhellou, L. (2017). Analyzing year-to-year changes in public transport passenger behavior using smart card data. Transportation Research Part C, 79, 274-289.
doi:10.1016/j.trc.2017.03.021
-Cai, Z., Li, T., Su, X., Guo, L., & Ding, Z. (2020). Research on Analysis Method of Characteristics Generation of Urban Rail Transit. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3608-3620. doi:10.1109/TITS.2019.2929619
-Chanal, D., Steiner, N., Raffaele, P., Chamagne, D., & Marion-Péra, M.-C. (2021). Online Diagnosis of PEM Fuel Cell by Fuzzy C-Means Clustering. In S. Elias, Reference Module in Earth Systems and Environmental Sciences. doi:10.1016/B978-0-12-819723-3.00099-8
-Chen, E., Ye, Z., Wang, C., & Zhang, W. (2019). Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data. Cities, 95, 102359. doi:10.1016/j.cities.2019.05.028
-Chen, R., Zhao, B., Wang, Y., Duan, Z., & Zhao, X. (2020). Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network. Sensors (Basel), 20(13), 3776. doi:10.3390/s20133776
-Cheng, Z., Trepanier, M., & Sun, L. (2019). Inferring trip destinations in transit smart card data using a probabilistic topic model. Montreal, Quebec, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT).
-Cheng, Z., Trépanier, M., & Sun, L. (2020). Probabilistic model for destination inference and travel pattern mining from smart card data. Transportation.doi:10.1007/s11116-020-10120-0
-Chopra, S., Dhiman, G., Sharma, A., Shabaz, D. M., Shukla, P., & Arora, M. (2021). Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences. Computational Intelligence and Neuroscience, 2021, 14. doi:10.1155/2021/6455592
-Chu, K. K., & Chapleau, R. (2010). Augmenting Transit Trip Characterization and Travel Behavior Comprehension. Transportation Research Board, 2183, 29-40. doi:10.3141/2183-04
-Chu, K., & Chapleau, R. (2008). Enriching Archived Smart Card Transaction Data for Transit Demand Modeling. Transportation Research Record Journal of the Transportation Research Board, 2063(1), 63-72. doi:10.3141/2063-08
-Cools, M., Moons, E., & Wets, G. (2010). Assessing the Impact of Public Holidays on Travel Time Expenditure. Transportation Research Record Journal of the Transportation Research Board, 2157(2157), 29-37. doi:10.3141/2157-04
-Dou, H., Liu, H., & Yang, X. (2007). OD Matrix Estimation Method of Public Transportation Flow Based on Passenger Boarding and Alighting. Journal of Transport Information and Safety, 25(2), 79-82. doi:10.3963/j.issn.1674-4861.2007.02.020
-Dou, M., He, T., Yin, H., Zhou, X., Chen, Z., & Luo, B. (2015). Predicting Passengers in Public Transportation Using Smart Card Data. Databases Theory and Applications. ADC. 9093, 28-40. Springer.
doi:10.1007/978-3-319-19548-3_3
-Edition, W. U. (2023, 08). http://www.demographia.com/db-worldua.pdf, 16. (DEMOGRAPHIA)
-El Mahrsi, M., Côme, E., Baro, J., & Oukhellou, L. (2014). Understanding Passenger Patterns in Public Transit through Smart Card and Socioeconomic Data: A Case Study in Rennes, France. The 3rd International Workshop on Urban Computing (UrbComp 2014), New York, 9-10.
-Essien, A., Petrounias, I., Sampaio, P., & Sampaio, S. (2018). The Impact of Rainfall and Temperature on Peak and Off-Peak Urban Traffic. 29th International Conference, DEXA 2018. Regensburg, Germany.
doi:10.1007/978-3-319-98812-2_36
-Ghalehkhondabi, I., Ardjmand, E., Young, W., & Weckman, G. (2019). A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. Journal of Tourism Futures, 5, 75-93.
doi:10.1108/JTF-10-2018-0061
-González, L., Perdiguero, J., & Sanz, À. (2021). Impact of public transport strikes on traffic and pollution in the city of Barcelona. Transportation Research Part D: Transport and Environment, 98, 102952. doi:10.1016/j.trd.2021.102952
-Gordon, J., Koutsopoulos, H., & Wilson, N. (2018). Estimation of population origin–interchange–destination flows on multimodal transit networks. Transportation Research Part C, 90, 350-365. doi:10.1016/j.trc.2018.03.007
-Gordon, J., Koutsopoulos, H., Wilson, N., & Attanucci, J. (2013). Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data. Transportation Research Record Journal of the Transportation Research Board, 2343(1), 17-24. doi:10.3141/2343-03
-Gössling, S., Neger, C., Steiger, R., & Bell, R. (2023). Weather, climate change, and transport: a review. Natural Hazards. doi:10.1007/s11069-023-06054-2
-Guzman, L., Beltran, C., Morales, R., & Sarmiento, O. (2023). Inequality in personal exposure to air pollution in transport microenvironments for commuters in Bogotá. Case Studies on Transport Policy, 11, 100963. doi:10.1016/j.cstp.2023.100963
-Hassanniakalager, A., Sermpinis, G., Stasinakis, C., & Verousis, T. (2020). A conditional fuzzy inference approach in forecasting. European Journal of Operational Research, 283(1), 196-216. doi:10.1016/j.ejor.2019.11.006
-He, L., & Trépanier, M. (2015). Estimating the Destination of Unlinked Trips in Transit Smart Card Fare Data. Transportation Research Record Journal of the Transportation Research Board, 2535, 97-104. doi:10.3141/2535-11
-Henke, I., Cartenì, A., Molitierno, C., & Errico, A. (2020). Decision-Making in the Transport Sector: A Sustainable Evaluation Method for Road Infrastructure. Sustainability, 12(3), 764. doi:0.3390/su12030764
-Hofmann, M., & O'Mahony, M. (2005). The Impact of Adverse Weather Conditions on Urban Bus Performance Measures. Intelligent Transportation Systems, 2005 IEEE. doi:10.1109/ITSC.2005.1520087
-Hu, N., Legara, E. F., Lee, K. K., Hung, G. G., & Monterola, C. (2016). Impacts of land use and amenities on public transport use, urban planning and design. Land Use Policy, 57, 356-367. doi:10.1016/j.landusepol.2016.06.004
-Hussain, E., Bhaskar, A., & Chung, E. (2021). Transit OD matrix estimation using smartcard data: Recent developments and future research challenges. Transportation Research Part C, 125, 103044. doi:10.1016/j.trc.2021.103044
-Ingvardson, J., Nielsen, O., Raveau, S., & Nielsen, B. (2018). Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis. Transportation Research Part C: Emerging Technologies, 90, 292-306. doi:10.1016/j.trc.2018.03.006
-Jang, S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. doi:10.1109/21.256541
-Joshi, S. (2020). Quantifying Impact of Weather Condition on Travel Time. School of City and Regional Planning. Georgia Institute of Technology.
Jung, J., & Sohn, K. (2017). Deep-learning Architecture to Forecast Destinations of Bus Passengers from Entry-only Smart-card Data. IET Intelligent Transport Systems, 11(6), 334-339. doi:10.1049/iet-its.2016.0276
-Kim, J., Corcoran, J., & Papamanolis, M. (2017). Route choice stickiness of public transport passengers: measuring habitual bus ridership behavior using smart card data. Transportation Research Part C: Emerging Technologies, 83, 146-164. doi:10.1016/j.trc.2017.08.005
-Kim, M.-K., Kim, S., & Sohn, H.-G. (2018). Relationship between Spatio-Temporal Travel Patterns Derived from Smart-Card Data and Local Environmental Characteristics of Seoul, Korea. Sustainability, 10(3), 787. doi:10.3390/su10030787
-Kumar, P., Khani, A., & He, Q. (2018). A robust method for estimating transit passenger trajectories using automated data. Transportation Research Part C: Emerging Technologies, 95(2063), 731-747. doi:10.1016/j.trc.2018.08.006
-Kusakabe, T., & Asakura, Y. (2014). Behavioral data mining of transit smart card data: a data fusion approach. Transportation Research Part C: Emerging Technologies, 46, 179-191.
doi:10.1016/j.trc.2014.05.012
-Lee, S., & Hickman, M. (2014). Trip purpose inference using automated fare collection data. Public Transport, 6, 1-20.
doi:10.1007/s12469-013-0077-5
-Li, T., Sun, D., Jing, P., & Yang, K. (2018). Smart Card Data Mining of Public Transport Destination: A Literature Review. Information, 9, 18. doi:10.3390/info9010018
-Liang, X., Wang, G., Min, M., Qi, Y., & Han, Z. (2019). A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction. Faculty Publications, 135. Retrieved from https://digitalscholarship.tsu.edu/facpubs/135
-Liu, W., Tan, Q., & Wu, W. (2020). Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning. Mathematical Problems in Engineering, 2020. doi:10.1155/2020/6625435
-Liyanage, S., & Dia, H. (2020). An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services. Sustainability, 12(10), 4117. doi:10.3390/su12104117
-Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P.-W. (2022). AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11, 365-380. doi:10.1016/j.jum.2022.05.002
-Liyanage, S., Dia, H., Abduljabbar, R., & Asadi Bagloee, S. (2019). Flexible Mobility On-Demand: An Environmental Scan. Sustainability, 11(5), 1262. doi:10.3390/su11051262
-Lu, J., Li, B., Li, H., & Al-Barakani, A. (2021). Expansion of city scale, traffic modes, traffic congestion, and air pollution. Cities, 108, 102974. doi:10.1016/j.cities.2020.102974
-Ma, X.-l., Wang, Y.-h., Chen, F., & Liu, J.-f. (2012). Transit smart card data mining for passenger origin information extraction. Journal of Zhejiang University Science C, 13(10), 750-760. doi:10.1631/jzus.C12a0049
-Ma, X.-l., Wu, Y.-J., Wang, Y., Chen, F., & Liu, J. (2013). Mining smart card data for transit riders’ travel patterns. Transportation Research Part C, 36, 1-12. doi:10.1016/j.trc.2013.07.010
-Medina, S. (2018). Inferring weekly primary activity patterns using public transport smart card data and a household travel survey. Travel Behaviour and Society, 12, 93-101. doi:10.1016/j.tbs.2016.11.005
-Milenkovic, M., Svadlenka, L., Melichar, V., Bojovic, N., & Avramovic, Z. (2018). SARIMA modelling approach for railway passenger flow forecasting. Transport, 33(5), 1113-1120. doi:10.3846/16484142.2016.1139623
Miller, P., De Barros, A., Kattan, L., & Wirasinghe, S. (2016a). Analyzing the sustainability performance of public transit. Transportation Research Part D: Transport and Environment, 44, 177-198. doi:10.1016/j.trd.2016.02.012
Mitra, A., Jain, A., Kishore, A., & Kumar, P. (2022). A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach. Operations Research Forum, 3(4), 2-22. doi:10.1007/s43069-022-00166-4
Mohammed, M., & Oke, J. (2023). Origin-destination inference in public transportation systems: A comprehensive review. International Journal of Transportation Science and Technology, 12(1), 315-328. doi:10.1016/j.ijtst.2022.03.002
Mosallanejad, M., Somenahalli, S., Vij, A., & Molls, D. (2019). An Approach to Distinguish Destination from the Alighting Stop based on Fare Data. Journal of the Eastern Asia Society for Transportation Studies, 13, 1348-1360.
Munizaga, M., & Palma, C. (2012). Estimation of disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C: Emerging Technologies, 24, 9-18.
doi:10.1016/j.trc.2012.01.007
Nagy, V., Horváth, B., & Horváth, R. (2017). Land-use zone estimation in public transport planning with data mining. Transportation Research Procedia, 27, 1050-1057. doi:10.1016/j.trpro.2017.12.145
Nam, D., Kim, H., Cho, J., & Jayakrishnan, R. (2017). A Model Based on Deep Learning for Predicting Travel Mode Choice. Transportation Research Board 96th Annual Meeting Transportation Research Board, 17-06512. Washington, DC.
Nassir, N., Khani, A., Lee, S., Noh, H., & Hickman, M. (2011). Transit Stop-Level Origin–Destination Estimation Through Use of Transit Schedule and Automated Data Collection System. Transportation Research Record Journal of the Transportation Research Board, 2263(1), 140-150. doi:10.3141/2263-16
Oort, N., Brands, T., & de Romph, E. (2015). Short-Term Prediction of Ridership on Public Transport with Smart Card Data. Transportation Research Record Journal of the Transportation Research Board, 2535(1), 105-111. doi:10.3141/2535-12
-Öztayşi, B., & Bolturk, E. (2014). Fuzzy Methods for Demand Forecasting in Supply Chain Management. Supply Chain Management under Fuzziness, 313, 243-268. doi:10.1007/978-3-642-53939-8_11
-Polson, N., & Sokolov, V. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1-17. doi:10.1016/j.trc.2017.02.024
-Razavi, S., Abbasian Najafabadi, T., & Mahmoodian, A. (2018). Remaining Useful Life Estimation Using ANFIS Algorithm: A Data-Driven Approach for Prognostics. 2018 Prognostics and System Health Management Conference (PHM-Chongqing), IEEE.522-526. doi:10.1109/PHM-Chongqing.2018.00095
Sarkar, P., & Mallikarjuna, C. (2013). Effect of Land Use on Travel Behaviour: A Case Study of Agartala City. Procedia - Social and Behavioral Sciences, 104, 533-542. doi:10.1016/j.sbspro.2013.11.147
-Tamblay, S., Galilea, P., Iglesias, P., Raveau, S., & Muñoz, J. C. (2016). A zonal inference model based on observed smart-card transactions for Santiago de Chile. Transportation Research Part A: Policy and Practice, 84, 44-54.
doi:10.1016/j.tra.2015.10.007
-Tin Kam Ho. (1995). Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition, 1, pp. 278-282. Montreal, QC, Canada. doi:10.1109/ICDAR.1995.598994
-Tin Kam Ho. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832-844. doi:10.1109/34.709601
-Trépanier, M., Chapleau, R., & Tranchant, N. (2007). Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System. Journal of Intelligent Transportation Systems: Technology, Planning and Operations, 11(1), 1-14. doi:10.1080/15472450601122256
-Wang, B., & Zheng, S. (2020). Air pollution lowers travel demand in a consumer city. Transportation Research Part D: Transport and Environment, 89, 102616. doi:10.1016/j.trd.2020.102616
-Wang, B., Shao, C., & Ji, X. (2017b). Influencing Mechanism Analysis of Holiday Activity–Travel Patterns on Transportation Energy Consumption and Emissions in China. Energies, 10(7). doi:10.3390/en10070897
-Wang, J., Lingzhi Yang, Y., & Zhou, B. (2017a). The OD matrix estimation model of passenger flow based on the POI around the bus station. Applied Decision Sciences, 10, 118-130.
doi:10.1504/IJADS.2017.084308
-Wang, W., Attanucci, J., & Wilson, N. (2011). Bus passenger origin-destination estimation and related analyses using automated data collection systems. Journal of Public Transportation, 14(4), 131-150.
Doi:10.5038/2375-0901.14.4.7
-Wang, Y., Almeida, G. H., de Romph, E., & Timmermans, H. (2017c). Using metro smart card data to model location choice of after-work activities: An application to Shanghai. Journal of Transport Geography, 63, 40-47. doi:10.1016/j.jtrangeo.2017.06.010
-Wu, W., Jiang, S., Liu, R., Jin, W., & Ma, C. (2020). Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model. Transportmetrica A Transport Science, 16(3), 359-387. doi:10.1080/23249935.2020.1711543
-Wu, W., Xia, Y., & Jin, W. (2021). Predicting Bus Passenger Flow and Prioritizing Influential Factors Using Multi-Source Data: Scaled Stacking Gradient Boosting Decision Trees. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2510-2523. doi:10.1109/TITS.2020.3035647
-Xiao, Y., Liu, J., Hu, Y., Wang, Y., Lai, K., & Wang, S. (2014). A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. Journal of Air Transport Management, 39, 1-11. doi:10.1016/j.jairtraman.2014.03.004
-Yao, E., Hong, J., Pan, L., Li, B., Yang, Y., & Guo, D. (2021). Forecasting Passenger Flow Distribution on Holidays for Urban Rail Transit Based on Destination Choice Behavior Analysis. Journal of Advanced Transportation, 2021.
doi:10.1155/2021/9922660
-Yu, J., & Yang, X.-G. (2006). Estimating a Transit Route OD Matrix from On-Off Data through an Artificial Neural Network Method. Applications of Advanced Technology in Transportation. The Ninth International Conference. Chicago Illinois, United States. doi:10.1061/40799(213)74
-Zhang, M., Guo, Y., & Ma, Y. (2014). A Probability Model of Transit OD Distribution Based on the Allure of Bus Station. Journal of Transport Information and Safety(3), 57-61. doi:10.3963/j.issn.1674-4861.2014.03.012
-Zhang, Y., & Xu, D. (2022). The bus is arriving: Population growth and public transportation ridership in rural America. Journal of Rural Studies, 95, 467-474. doi:10.1016/j.jrurstud.2022.09.018
-Zhao, J., Rahbee, A., & Wilson, N. (2007). Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection Systems. Computer-Aided Civil and Infrastructure Engineering, 22(5), 376-387. doi:10.1111/j.1467-8667.2007.00494.x
-Zhao, J., Zhang, F., Tu, L., Xu, C., Shen, D., Tian, C., Li, Z. (2017). Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems. IEEE Transactions on Intelligent Transportation Systems, 18, 790-801.
doi:10.1109/TITS.2016.2587864
-Zhao, Z., Koutsopoulos, H., & Zhao, J. (2018). Individual mobility prediction using transit smart card data. Transportation Research Part C, 89, 19-34. doi:10.1016/j.trc.2018.01.022
-Zhou, M., Wang, D., Li, Q., Yue, Y., Tu, W., & Cao, R. (2017). Impacts of weather on public transport ridership: Results from mining data from different sources. Transportation Research Part C: Emerging Technologies, 75, 17-29. doi:10.1016/j.trc.2016.12.001
-Zhou, X., Yang, X., & Wu, X. (2012). Origin-destination matrix estimation method of public transportaion flow based on data from bus integrated-circuit cards. J. Tongji Univ, 40, 1027-1030.
Doi:10.3969/j.issn.0253-374x.2012.07.011
-Zhou, Y., Qian, C., Xiao, H., Xin, J., Wei, Z., & Feng, Q. (2019). Coupling Research on Land Use and Travel Behaviors Along the Tram Based on Accessibility Measurement -Taking Nanjing Chilin Tram Line1 as an Example. Sustainability, 11(7), 2034.
Doi:10.3390/su11072034
-Zhou, Y., Thill, J.-C., Xu, Y., & Fang, Z. (2021). Variability in individual home-work activity patterns. Journal of Transport Geography, 90, 102901. doi:10.1016/j.jtrangeo.2020.102901
