Long-term Implications (2055 and later)

The long-term impacts of AVs mainly affect location choices and land-use patterns. It is theorized that residential, work, and school location selections are likely to shift after AVs are gradually introduced to the network. This mainly stems from the consequent benefits such as relaxed driving, less congested network, higher speed profiles and shorter travel times, which could be interpreted as an overall reduction in travel costs. Therefore, people can traverse longer distances with little to no difference in their associated general travel costs. This provides users with more flexible residential, work, and school location choice sets, which can bring about a variety of economic and social benefits (Anderson et al. 2014; Labi and Saeed, 2015).

In a speculative study, Anderson et al. (2014) discussed the contradictory scenarios of AV impacts on land use. Based on the discussion in this study, transportation cost will be decreased considerably because drivers and passengers would be able to do tasks other than driving in the car while being driven to the destination in an autonomous vehicle. This cost reduction may free a balance of household budgets consequently a group of people may be able to afford larger houses in better residential lands. Also because the driving task would be much easier and farther distance can be driven in shorter time, a group may

decide to relocate from urban area to suburban area. On the other hand, the considerable reduction in parking demand can provide an attractiveness for people to move and live in urban areas while they do not need to pay for parking anymore and live closer to their jobs. This is also feasible assuming demand-responsive driverless taxis which do not need parking and a car’s capability to self-park outside the urban area. This hypothesis was supported by Snyder (2014) which concluded with the ability of autonomous vehicles to drop passenger and look for parking space outside Central Business District a huge amount of pressure for building parking for each destination will be removed, consequently considerable space in the high priced land area can be freed. This may result in reduced land price in urban area which is an attractive feature for relocation.

The elimination/change of parking spaces is not limited to parking garages only, but on-street parking infrastructures will also change to AV specific drop-off locations according to a recent discussion based report by Chapin et al. (2016). Based on the discussion, this technology has the potential to eliminate the need to driver and passenger to seat in the vehicle while looking for a parking, thus farther and cheaper areas for parking space will be also attractive. Since the drop-off and pick-up locations can be used common between AVs, mass transit systems and ridesourcing vehicles (i.e. Uber), the existing mass transit stops can be updated for this purpose. The authors mentioned that a safe waiting area should be considered for passengers to handle this change safely.

Pendyala and Bhat (2014) discussed some of the hypothetical impacts of driverless cars. Authors discussed that travel time and distance will play less important role that now in future transportation with smart vehicles. This change will result in looking for a wider

area to access better residential locations, jobs and schools, which may change overall urban development patterns Similar patterns have been suggested by several other sources (Lari and Onyiah, 2015; Alessandrini et al., 2014; Alessandrini et al., 2015).

Based on speculations, complete street concept will be more applicable and attractive in the AV period (Chapin et al., 2016). The smart concept in an autonomous vehicle such as lane change warrant provides an opportunity to design narrower lanes for AV fleeting, consequently more space can be assigned to bicycle and pedestrian modes. This will be an important matter especially in downtown area, not only because land price is high in business districts, but because of lack of space, normally pedestrian and bike safety is compromised for vehicular fleet, which can be avoided in a complete street. Figure 2-1 shows a hypothetical intersection before and after AV introduction. Except aforementioned changes, several pavement marking and signs will be replaced by Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications.

Analytical studies mainly confirm the discussed hypotheses. Zhang et al. (2015) explored the effect of Shared Autonomous Vehicles (SAVs) on urban area parking demand using an agent-based model. The agent based model results showed that if only 2% of the hypothetical population adopt SAV system, the parking demand can be reduced by 90% for those adopted households. Although the model did not explore some important features regarding parking such as parking price, but even not considering any changes in these features, results support the idea that parking demand would be considerably reduced when more people adopt autonomous vehicle and shared taxies.

In a comprehensive parking management report, Litman (2012) estimated annual parking costs including land, construction, maintenance, and operation for CBDs, other central/urban areas and suburban areas. Based on the estimations by Litman (2012), relocating one parking lot from CBD to a non-CBD urban area can save close to $2,000 annually, which increases to $3,000 if the parking space is relocated to suburban area. This should be noticed that because of more carsharing programs in future due to AVs development, there is no need to provide same parking spaces that are actually removed from CBD area in non-CBD locations, which means saving more money. Litman (2014) study concludes each new AV will result in $250 in parking saving assuming 10% of AVs being publicly shared.

Another agent-based modeling simulation were developed by Kim et al. (2015) to explore the market penetration and potential impacts of AVs in Korea for long range. Assumptions of this study was based on Litman (2014) market penetration model and Yokota (1998) recommendations for road capacity changes due to AVs. Different years were assumed for road opening for AVs; i.e. 2020 for highways and 2050 for arterials. Two scenarios including current urban growth, and 100% AV adoption for year 2070 were explored in this study. Findings supported the hypothesis that residents do not prefer to locate close to urban center and more dispersed distributions of population was seen.