Skip to main content
SpringerLink
Log in

Simulating and analyzing the effect on travel behavior of residential relocation and corresponding traffic demand management strategies

  • Transportation Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope Submit manuscript

Abstract

Triggered by rapid urban expansion and fast population growth, a progressive residential relocation has occurred in most cities and its impacts on travel behavior have been confirmed in many studies. However, none has evaluated the effects of travel management strategies that relieves the side effects caused by this relocation. To this end, a multi-agent-based simulation model is proposed to assess the impacts of residential relocation on travel behavior and urban transportation in China. Based on the data in Tongling, China, the simulation on six scenarios is conducted to test how the residents in the urban center and suburbs are affected by different strategies, such as increased land diversity in suburbs, lowered growth in private car ownership and improved public transit accessibility. The results indicate that more daily trips would be lengthened and tend to be motorized by this residential relocation. The scenario test shows that compared to other strategies, policies that aims to reduce travel demand and trip distances after residential relocation have a better performance in traffic improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K., and Axhausen, K. (2009). “MATSim-T: Architecture and simulation times.” Multi-agent Systems for Traffic and Transportation Engineering, pp. 57–78, DOI: 10.4018/978-1-60566-226-8.ch003.

    Chapter  Google Scholar 

  • Bazzan, A. L., de Brito do Amarante, M., and Da Costa, F. B. (2012). “Management of demand and routing in autonomous personal transportation.” Journal of Intelligent Transportation Systems, Vol. 16, No. 1, pp. 1–11, DOI: 10.1080/15472450.2012.639635.

    Article  Google Scholar 

  • Beutel, M. C., Addicks, S., Zaunbrecher, B. S., and Himmel, S. (2015). “Agent-based transportation: Demand management demand effects of reserved parking space and priority lanes in comparison and combination.” Smart Cities and Green ICT Systems (SMARTGREENS), 2015 International Conference on IEEE, pp. 1–7, DOI: 10.5220/0005411503170323.

    Google Scholar 

  • Boarnet, M. G. (2011). “A broader context for land use and travel behavior, and a research agenda.” Journal of the American Planning Association, Vol. 77, No. 3, pp. 197–213, DOI: 10.1080/01944363. 2011.593483.

    Article  Google Scholar 

  • Cervero, R. and Day, J. (2008). “Suburbanization and transit-oriented development in China.” Transport Policy, Vol. 15, No. 5, pp. 315–323, DOI: 10.1016/j.tranpol.2008.12.011.

    Article  Google Scholar 

  • Charypar, D. and Nagel, K. (2005). “Q-learning for flexible learning of daily activity plans.” Transportation Research Record: Journal of the Transportation Research Board (1935), pp. 163–169, DOI: 10.3141/1935-19.

    Google Scholar 

  • Chen, C., Gong, H., and Paaswell, R. (2008). “Role of the built environment on mode choice decisions: Additional evidence on the impact of density.” Transportation, Vol. 35, No. 3, pp. 285–299, DOI: 10.1007/s11116-007-9153-5.

    Article  Google Scholar 

  • Crane, R. and Crepeau, R. (1998). “Does neighborhood design influence travel? A behavioral analysis of travel diary and GIS data.” Transportation Research Part D: Transport and Environment, Vol. 3, No. 7, pp. 225–238, DOI: 10.1016/s1361-9209(98)00001-7.

    Article  Google Scholar 

  • Eliasson, J. and Mattsson, L. G. (2001) “Transport and location effects of road pricing: A simulation approach.” Journal of Transport Economics and Policy, Vol. 35, No. 3, pp. 417–456.

    Google Scholar 

  • Figueroa, M. J., Nielsen, T. A. S., and Siren, A. (2014). “Comparing urban form correlations of the travel patterns of older and younger adults.” Transport Policy, Vol. 35, pp. 10–20, DOI: 10.1016/j.tranpol.2014.05.007.

    Article  Google Scholar 

  • Frank, L. D., Sallis, J. F., Conway, T. L., Chapman, J. E., Saelens, B. E., and Bachman, W. (2006). “Many pathways from land use to health: Associations between neighborhood walkability and active transportation, body mass index, and air quality.” Journal of the American Planning Association, Vol. 72, No. 1, pp. 75–87, DOI: 10.1080/01944360608976725.

    Article  Google Scholar 

  • Gim, T. H. T. (2013). “The relationships between land use measures and travel behavior: A meta-analytic approach.” Transportation Planning & Technology, Vol. 36, No. 5, pp. 413–434, DOI: 10.1080/03081060.2013.818272.

    Article  Google Scholar 

  • Handy, S. L. and Clifton, K. J. (2001). “Local shopping as a strategy for reducing automobile travel.” Transportation, Vol. 28, No. 4, pp. 317–346, DOI: 10.1023/A:1011850618753.

    Article  Google Scholar 

  • Hensher, D. A. and Ton, T. T. (2000) “A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice.” Transportation Research Part E: Logistics and Transportation Review, Vol. 36, No. 3, pp. 155–172, DOI: 10.1016/s1366-5545(99)00030-7.

    Article  Google Scholar 

  • Janssen, D., Lan, Y., Wets, G., and Chen, G. (2007). “Allocating time and location information to activity-travel patterns through reinforcement learning.” Knowledge-Based Systems, Vol. 20, No. 5, pp. 466–477, DOI: 10.1016/j.knosys.2007.01.008.

    Article  Google Scholar 

  • Jennings, N. R., Sycara, K., and Wooldridge, M. (1998). “A roadmap of agent research and development.” Autonomous Agents and Multiagent Systems, Vol. 1, No. 1, pp. 7–38, DOI: 10.1023/a:1010090405266.

    Article  Google Scholar 

  • Joubert, J., Fourie, P., and Axhausen, K. (2010). “Large-scale agentbased combined traffic simulation of private cars and commercial vehicles.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2168, pp. 24–32, DOI: 10.3141/2168-04.

    Article  Google Scholar 

  • Kahn, M. E. (2000). “The environmental impact of suburbanization.” Journal of Policy Analysis and Management, Vol. 19, No. 4, pp. 569–586, DOI: 10.1002/1520-6688(200023)19:4<569::AID-PAM3>3.0.CO;2-P.

    Article  Google Scholar 

  • Krizek, K. J. (2011). “Residential relocation and changes in urban travel: Does neighborhood-scale urban form matter?.” Journal of the American Planning Association, Vol. 69, No. 3, pp. 265–281, DOI: 10.1080/01944360308978019.

    Article  Google Scholar 

  • Maat, K. and Timmermans, H. (2006). “Influence of land use on tour complexity––a Dutch case.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1977, pp. 234–241, DOI: 10.3141/1977-29.

    Article  Google Scholar 

  • Mieszkowski, P. and Mills, E. S. (1993). “The causes of metropolitan suburbanization.” The Journal of Economic Perspectives, Vol. 7, No. 3, pp. 135–147, DOI: 10.1257/jep.7.3.135.

    Article  Google Scholar 

  • Næss, P. (2005). “Residential location affects travel behavior—but how and why? The case of Copenhagen Metropolitan Area.” Progress in Planning, Vol. 63, No. 2, pp. 165–257, DOI: 10.1016/j.progress.2004.07.004.

    Article  Google Scholar 

  • Navarro, L., Flacher, F., and Corruble, V. (2011). “Dynamic level of detail for large scale agent-based urban simulations.” The 10th International Conference on Autonomous Agents and Multiagent Systems-Vol. 2. International Foundation for Autonomous Agents and Multiagent Systems, Vol. 2011, pp. 701–708.

    Google Scholar 

  • Prillwitz, J., Harms, S., and Lanzendorf, M. (2007). “Interactions between residential relocations, life course events, and daily commute distances.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2021, pp. 64–69, DOI: 10.3141/2021-08.

    Article  Google Scholar 

  • Reid, E., Pendall, R., and Chen, D. (2003). “Measuring sprawl and its transportation impacts.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1831, No. 1, pp. 175–183, DOI: 10.3141/1831-20.

    Google Scholar 

  • Rolla, V. G. and Curado, M. (2013). “A reinforcement learning-based routing for delay tolerant networks.” Engineering Applications of Artificial Intelligence, Vol. 26, No. 10, pp. 2243–2250, DOI: 10.1016/j.engappai.2013.07.017.

    Article  Google Scholar 

  • Saelens, B. E., Sallis, J. F., and Frank, L. D., (2003) “Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures.” Annals of Behavioral Medicine, Vol. 25, No. 2, pp. 80–91, DOI: 10.1207/s15324796abm2502_03.

    Article  Google Scholar 

  • Scheiner, J. and Holz-Rau, C. (2013). “Changes in travel mode use after residential relocation: A contribution to mobility biographies.” Transportation, Vol. 40, No. 2, pp. 431–458, DOI: 10.1007/s11116-012-9417-6.

    Article  Google Scholar 

  • Shay, E. A. and Khattak, J. (2007). “Automobiles, trips, and neighborhood Type: Comparing environmental measures.” Transportation Research Record Journal of the Transportation Research Board, Vol. 43, No. 1, pp. 75–84, DOI: 10.3141/2010-09.

    Google Scholar 

  • Shiftan, Y. (2008). “The use of activity-based modeling to analyze the effect of land-use policies on travel behavior.” Annals of Regional Science, Vol. 42, No. 1, pp. 79–97, DOI: 10.1007/s00168-007-0139-1.

    Article  Google Scholar 

  • Silva, J. D. A. E., Morency, C., and Goulias, K. G. (2012). “Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal.” Transportation Research Part A, Vol. 46, No. 8, pp. 1252–1264, DOI: 10.1016/j.tra.2012.05.003.

    Google Scholar 

  • Subba Rao P. V., Sikdar, P. K., Krishna Rao, K.V., and Dhingra, S. L. (1998). “Another insight into artificial neural networks through behavioural analysis of access mode choice.” Computers, Environment & surban Systems, Vol. 22, No. 5, pp. 485–496, DOI: 10.1016/s0198-9715(98) 00036-2.

    Article  Google Scholar 

  • Tortum, A., Yayla, N., and Gökdağ, M. (2009). “The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system.” Expert Systems with Applications, Vol. 36, No. 3, pp. 6199–6217, DOI: 10.1016/j.eswa.2008.07.032.

    Article  Google Scholar 

  • Vanhulsel, M., Janssens, D., and Wets, G. (2007). Calibrating a new reinforcement learning mechanism for modeling dynamic activitytravel behavior and key events, Transportation Research Board CDROM.

    Google Scholar 

  • Waraich, R. A., Charypar, D., Balmer, M., and Axhausen, K. W. (2015) “Performance improvements for large-scale traffic simulation in MATSim.” Computational Approaches for Urban Environments. Springer International Publishing, pp. 211–233, DOI: 10.1007/978-3-319-11469-9_9.

    Chapter  Google Scholar 

  • Watkins, C. J. and Dayan, P. (1992) “Q-learning.” Machine Learning, Vol. 8, Nos. 3–4, pp. 279–292, DOI: 10.1007/bf00992698.

    MATH  Google Scholar 

  • Yang, M., Zhao, J., Wang, W., Liu, Z., and Li, Z. (2015). “Metro commuters’ satisfaction in multi-type access and egress transferring groups.” Transportation Research Part D Transport & Environment, Vol. 34, pp. 179–194, DOI: 10.1016/j.trd.2014.11.004.

    Article  Google Scholar 

  • Yang, L., Zheng, G., and Zhu, X. (2013). “Cross-nested logit model for the joint choice of residential location, travel mode, and departure time.” Habitat International, Vol. 38, pp. 157–166, DOI: 10.1016/j.habitatint.2012.06.002.

    Article  Google Scholar 

  • Yue, W. Z., Liu, Y., and Fan, P. L. (2013). “Measuring urban sprawl and its drivers in large Chinese cities: The case of Hangzhou.” Land Use Policy, Vol. 31, No. 2, pp. 358–370, DOI: 10.1016/j.landusepol. 2012.07.018.

    Article  Google Scholar 

  • Zolfpour-Arokhlo, M., Selamat, A., Mohd Hashim, S. Z., and Afkhami, H. (2014). “Modeling of route planning system based on Q valuebased dynamic programming with multi-agent reinforcement learning algorithms.” Engineering Applications of Artificial Intelligence, Vol. 29, No. 3, pp. 163–177, DOI: 10.1016/j.engappai.2014.01.001.

    Article  Google Scholar 

  • Zhu, S., Levinson, D. M., and Zhang, L. (2008). “Agent-based route choice with learning and exchange of information.” Transportation Research Board 87th Annual Meeting. No. 08-2152.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing, 210096, China

    Haoyang Ding

  2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Si Pai Lou #2, Nanjing, 210096, China

    Haoyang Ding

  3. School of Transportation, Southeast University, Si Pai Lou #2, Nanjing, 210096, China

    Haoyang Ding, Min Yang, Wei Wang & Chengcheng Xu

Authors
  1. Haoyang Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chengcheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Min Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, H., Yang, M., Wang, W. et al. Simulating and analyzing the effect on travel behavior of residential relocation and corresponding traffic demand management strategies. KSCE J Civ Eng 22, 837–849 (2018). https://doi.org/10.1007/s12205-017-0798-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-017-0798-0

Keywords

Search

Navigation