Collaborative Research: Modeling and Analysis of Advanced Parking Management for Traffic Congestion Mitigation (CMMI #1724168)

Intro­duc­tion

Park­ing is a grow­ing prob­lem in dense urban areas. To many, find­ing a park­ing space in these areas is an unpleas­ant expe­ri­ence of uncer­tain­ty and frus­tra­tion. Cruis­ing for park­ing makes traf­fic on already-con­gest­ed urban streets even worse and leads to sig­nif­i­cant waste in time and fuel. In trans­porta­tion, smart­phone-based park­ing man­age­ment appli­ca­tions have emerged. These appli­ca­tions help dri­vers find park­ing spaces by allow­ing them to use smart­phones to view real-time avail­abil­i­ty and prices of park­ing spaces and guide them to open park­ing spaces, reserved or oth­er­wise. This award devel­ops the­o­ret­i­cal foun­da­tions and method­olo­gies for ana­lyz­ing these emerg­ing park­ing man­age­ment ser­vices. Results from this research pro­vide a bet­ter under­stand­ing of the impacts of advanced park­ing man­age­ment ser­vices on park­ing com­pe­ti­tion and trav­el pat­terns. The research devel­ops poli­cies to reduce traf­fic con­ges­tion and emis­sions in dense urban areas. This award pos­i­tive­ly impacts engi­neer­ing edu­ca­tion by offer­ing new mate­ri­als and case stud­ies and engag­ing under­rep­re­sent­ed stu­dent groups in research.

Using game-the­o­ret­ic, dynam­ic and sto­chas­tic pro­gram­ming approach­es to inves­ti­gate both tem­po­ral and spa­tial trav­el pat­terns with advanced park­ing man­age­ment, this project gen­er­ates a set of ana­lyt­i­cal tools that explain the under­ly­ing work­ing mech­a­nisms of advanced park­ing man­age­ment ser­vices and gauge their poten­tial for reduc­ing traf­fic con­ges­tion. The the­o­ret­i­cal efforts in this research are com­ple­ment­ed by an agent-based sim­u­la­tion, which tests the valid­i­ty and applic­a­bil­i­ty of the the­o­ries, and unveils com­plex out­comes of park­ing com­pe­ti­tion under real­is­tic park­ing search behav­iors. This work advances the knowl­edge and analy­sis of park­ing man­age­ment and enrich­es the lit­er­a­ture of mod­el­ing morn­ing com­mute and vehi­cle routing.

Sim­u­la­tion

We apply Net­L­o­go to con­struct agent-based sim­u­la­tion mod­els to ver­i­fy the pre­dic­tions and insights drawn from the pro­posed the­o­ret­i­cal tools con­sid­er­ing dif­fer­ent park­ing man­age­ment ser­vices, such as park­ing infor­ma­tion, reser­va­tion, as well as sta­tus quo.

Specif­i­cal­ly, we con­sid­er a long one-way street where the des­ti­na­tion is locat­ed and on-street park­ing spaces are scat­tered around the des­ti­na­tion. Dri­vers who go to the des­ti­na­tion need to select one of the avail­able spaces to park. They are assumed to make this selec­tion in a way that min­i­mizes their walk­ing time to the des­ti­na­tion. Fur­ther, because the street is one-way and very long, once a dri­ver has passed a park­ing space, she would not be able to come back for it.

Sta­tus quo: since dri­vers have no access to park­ing infor­ma­tion or reser­va­tion ser­vices, we assume that they will start cruis­ing for park­ing at dif­fer­ent spaces based on their dif­fer­ent risk-tak­ing atti­tudes. For exam­ple, risk-averse dirvers pre­fer to start cruis­ing for park­ing at spaces far from their des­ti­na­tion, while risk-seek­ing dri­vers may dri­ve very close to the des­ti­na­tion and start from there.

Infor­ma­tion: although dri­vers have access to an infor­ma­tion ser­vice, it may not be wise for dri­vers to sim­ply dri­ve to the best cur­rent­ly avail­able space, because anoth­er dri­ver before them may take the space and oth­er occu­pied spaces may soon become vacant. There­fore, we assume that dri­vers will start cruis­ing for park­ing at a par­tic­u­lar space and then take the first vacant space and walk to the final destination.

Reser­va­tion: since dri­vers are allowed to reserve park­ing spaces, they are assumed to reserve the vacant one clos­est to the des­ti­na­tion when they enter the street.

Sta­tus-quo parking
Park­ing with information
Park­ing with reservation

Pub­li­ca­tions