Final Project Written Report

Our analysis of South Jersey has shown a lot about the future feasibility of an actual aTaxi system running within the state of New Jersey, and even speculative scalability for the entire United States. After comparing and contrasting the data and graphs from each of our counties, it seems that Camden County represents the best characteristics necessary for a project like this to work. Its population density is key for efficiency because it allows for the most riders to share the most amount of aTaxis, thus limiting the amount of total taxis the company will have to purchase as well as limiting total gas usage. That being said, Gloucester and Atlantic county both also can operate a system like this, however, it will require more single-person rides overall. Atlantic County shows the most promise for aTaxis in the future because of Etess Arena & its surrounding areas’ seeming happiness to adopt an aTaxi system, given the strictly objective data we have produced demonstrating a potential for shared ridership and number of trips required.

It seems that a DD = 300 seconds (5 minutes) is the most feasible time to ask people to wait for their aTaxi. Any longer and an informal poll of potential riders reveals that the waiting time vs. travel time ratio would be too high, and thus we would lose ridership. Any lower, and it might become economically infeasible due to the cost of providing more taxis to more places faster. The amount of people per taxi will fluctuate according to demand. Our graphs of riders per hour can help determine the exact amount during a time span per pixel. Since we only analyzed the most departures pixels, it is clear that there will be a unimodal or bi-modal demand for bigger taxis (vans that can fit between 5-8) during the rush periods, and otherwise sedans would seem to get the job done. The amount of taxis at a certain time will also depend on those graphs, as there seems to be lulls during the early morning (midnight-5am) and midday (10am-2pm) for many of the biggest pixels.

With this information, we are certainly on track to developing the perfect model to autonomously and efficiently transport New Jersey’s population around the state. Due to the diverse nature of all the counties, moreover, it provides us with great opportunity for scalability for the rest of the USA.