| 000 | 02456nam a2200253 4500 | ||
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| 999 |
_c11595 _d11595 |
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| 003 | OSt | ||
| 005 | 20210331160025.0 | ||
| 007 | cr aa aaaaa | ||
| 008 | 210331b ||||| |||| 00| 0 eng d | ||
| 100 |
_aHuang, Ruihong _943439 |
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| 245 | _aSimulating individual work trips for transit-facilitated accessibility study | ||
| 260 |
_bSage, _c2019. |
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| 300 | _aVol 46, Issue 1, 2019,(84-102 p.) | ||
| 520 | _aTo measure job accessibility, person-based approaches have the advantage to capture all accessibility components: land use, transportation system, individual’s mobility and travel preference, as well as individual’s space and time constraints. This makes person-based approaches more favorable than traditional aggregated approaches in recent years. However, person-based accessibility measures require detailed individual trip data which are very difficult and expensive to acquire, especially at large scales. In addition, traveling by public transportation is a highly time sensitive activity, which can hardly be handled by traditional accessibility measures. This paper presents an agent-based model for simulating individual work trips in hoping to provide an alternative or supplementary solution to person-based accessibility study. In the model, population is simulated as three levels of agents: census tracts, households, and individual workers. And job opportunities (businesses) are simulated as employer agents. Census tract agents have the ability to generate household and worker agents based on their demographic profiles and a road network. Worker agents are the most active agents that can search jobs and find the best paths for commuting. Employer agents can estimate the number of transit-dependent employees, hire workers, and update vacancies. A case study is conducted in the Milwaukee metropolitan area in Wisconsin. Several person-based accessibility measures are computed based on simulated trips, which disclose low accessibility inner city neighborhoods well covered by a transit network. | ||
| 650 |
_aGeographic information systems, _945602 |
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| 650 |
_a accessibility, _945603 |
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| 650 |
_aagent-based modeling, _939569 |
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| 650 |
_awork trip, _945604 |
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| 650 |
_atransit, _945605 |
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| 650 |
_asimulation _945606 |
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| 773 | 0 |
_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science |
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| 856 | _uhttps://doi.org/10.1177/2399808317702148 | ||
| 942 |
_2ddc _cART |
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