“Smart driving technologies” are components that create a more intelligent automotive system, and these technologies can be beneficial in the future for our infrastructure. To analyze these technologies, we anticipated benefits relating to transportation safety, mobility and environment. This involves crash benefits, travel time and congestion benefits, and several cost benefits, amongst others. Aligned with this vision and as part of the TxDOT Project 0-6847 “An Assessment of Autonomous Vehicles: Traffic Impacts and Infrastructure Needs”, the objective of this report is to provide a systematic synthesis of contemporary smart driving technologies, including their technological maturity and their potential impacts. The project began by understanding the current state-of-practice and trends. NHTSA provides a four-level taxonomy for automated vehicles, which was used to classify smart driving technologies and infrastructure needs. Level 0 and Level 1 technology, such as blind spot monitoring and electric stability control, are already entering mainstream adoption. Level 2 technology is promising for the future featuring technologies such as adaptive cruise control (ACC) in conjunction with lane centering or lane keeping assist (LKA). Level 3 and Level 4 technologies, however, have yet to be adopted in the mainstream and pose several large barriers to adoption due to uncertainty in performance and real-world driving. Each of these levels faces many barriers, but the main barriers for such technologies are cost, reliability, and legislation. A large issue with all levels involves cost. However, cost tends to decrease over time and much like cost, we expect that market penetration of these technologies will increase rapidly. Another major barrier involved in this technology’s adoption involves licensing and testing standards within the U.S., which are currently being developed at the state level, delaying large-scale adoption. Some technology will also require more information from roadways and needs supporting infrastructural components to function properly such as lane markings and signs. Regarding this need, TxDOT has a key role to play in facilitating the arrival of a substantial presence, which will have many economic and quality-of-life impacts. The project used surveys to analyze and gain an understanding of the U.S. general public’s perception towards such technologies and their willingness to adopt such technologies. The team designed and disseminated a Texas-wide survey for 1,364 completed responses and used those data in the proposed fleet evolution framework to simulate Texans’ long-term (2015 to 2045) adoption of connected and autonomous vehicle (CAV) technologies under different technology pricing scenarios (1%, 5%, and 10% annual price-reduction rates). Within the surveys, respondents were asked several anticipatory questions including their vehicle history as well as their future vehicle plans, their technology preferences (buying/selling their vehicles or simply adding new technologies to their current vehicles), and their comfort and willingness to pay (WTP) towards connected and autonomous vehicles. The team found that advanced automation technologies are not yet popular. More than half of the respondents are not willing to pay anything to add the advanced automation technologies such as self-parking valet, limited self-driving [Level 3], and full-self driving [Level 4]. We also found that among single-function (Level 1) and combined-function (Level 2) automation technologies, traffic sign recognition is the least appealing (52.5% of respondents reported $0 WTP), currently least adopted (2%), and anticipated to have the least future adoption (in 2045) by Texans. Blind-spot monitoring and emergency automated braking are the two most appealing technologies for Texans, with the highest adoption rate (59.4%) among Level 1 and Level 2 technologies in 2045 at a 10% annual price-reduction rate. The future adoption rate of connectivity (for DSRC-based basic safety messaging) is estimated to be 57.9% under 10% yearly price reduction scenario. However, it was also found that self-driving valet services and full self-driving (Level 4) technology is estimated to reach adoption rates of just 34.8% and 38.5% respectively and limited self-driving (Level 3) is estimated to be the least popular at a 16.9% adoption rate. Although, Level 3 autonomy may be largely “skipped” by manufacturers (due to difficulties in quickly getting drivers sufficiently context-aware to take over control in situations where the AV technology needs human assistance). Finally, average WTP (of the respondents with a non- zero WTP) to add connectivity, and Level 3 and Level 4 upgrades to their vehicles (new or existing) are $110, $5,551, and $14,589, respectively. Overall, without people’s WTP rising (thanks to good experiences by peers owning such technologies), policies that promote (and/or require such technologies), or unusually fast reductions in technology costs, it is unlikely that technology will be anywhere near homogeneous by 2045. This research report also describes the potential crash, congestion and other impacts of CAVs in Texas, and provides initial monetary estimates of those impacts, at various levels of market penetration. In this report, it is anticipated that CAVs will lead to increased vehicle miles traveled (VMT) because, essentially, drivers experience falling travel time burdens. Their values of travel time that make using a vehicle “costly” tend to decrease because they are more comfortable heading to more distant locations (may consider replacing air travel with highway travel) and those unable to drive themselves such as the handicapped can now safely travel. Even trucking can become more competitive compared to rail transport through train due to removing driver costs from the scenario. Shared autonomous vehicles (SAVs) may also emerge as a new transportation mode, meaning that some AVs act as driverless taxis or shuttles. In accordance to safety concerns of the driving world, CAVs will likely be safer than human drivers, since human errors are a factor in over 90 percent of U.S. crashes. Results from this project suggest that more than 2,400 lives could be saved each year on Texas roadways by the time 90% market penetration is reached, with over $14 billion in economic savings, or more than $62 billion in comprehensive crash costs (a 75% total reduction in comprehensive crash costs). In terms of cost savings per driver that shifts to CAV operation, around $1,357 per year in added productivity and leisure time can be gained. When comparing these potential impacts over the life of a CAV against the anticipated costs of communication and automation, the net benefits of CAVs appear quite strong. At the 10% market penetration level, privately owned and operated CAVs could have a net present value (NPV) of nearly $13,960 per vehicle, increasing to an estimated value of $27,000 with 90% market penetration. This research report also describes the potential crash, congestion and other impacts of CAVs in Texas, and provides initial monetary estimates of those impacts, at various levels of market penetration. In this report, it is anticipated that CAVs will lead to increased vehicle miles traveled (VMT) because, essentially, drivers experience falling travel time burdens. Their values of travel time that make using a vehicle “costly” tend to decrease because they are more comfortable heading to more distant locations (may consider replacing air travel with highway travel) and those unable to drive themselves such as the handicapped can now safely travel. Repositioning trips entail AVs dropping off passengers at their destinations and then returning to their owner’s residence (or another location) for free parking, thereby reducing the cost of driving, relative to transit and other alternatives. To anticipate how these behaviors will combine to affect traffic, we created a four-step model using a generalized-cost function of travel time, monetary costs (like parking charges and tolls), and fuel costs. The fact that travel costs impact trip-making decisions, mode choices, and route choices is well-known and fundamental to most travel demand modeling efforts. Three mode choices of driving and parking (using an AV or HV), traveling in a repositioning AV, and transit are considered in the four-step model, with AVs possibly affecting all three choices. The four-step model for determining the impact of AVs allowed for some variation in trip generation, trip distribution, mode choice, and traffic assignment. Trip generation involves estimating trip productions and attractions for each zone. Trip distribution involves splitting a known volume of person-trips and assigning each to a destination. Mode choice determines whether vehicles are assigned to be parking, repositioning, or in transit mode. The second part of our team’s travel demand modeling work examines the use of SAVs with dynamic traffic assignment (DTA) (rather than standard, static). DTA allows for generally more realistic with demand changing with time and congestion-feedback models. Along with DTA, to reflect the introduction of AVs on roads shared with HVs, new flow models were developed including a cell transmission model (CTM). The model assumes that all vehicles in the same cell travel at the same speed, class-specific density is uniformly distributed within cells, and backwards wave speed is less than or equal to free-flow speed. The multi-class CTM is shown to be consistent with the hydrodynamic theory. A CTM modeled link flows in our DTA simulations. In these DTA simulations we used a multi-class CTM that admits variations in capacity and backwards wave speed in response to class proportions within each cell (or “sub-link” of the network). The simulation-based dynamic traffic assignment (SBDTA) model involves three main components: a traffic simulator, path generator, and assignment module. As this DTA using a multi-class CTM is more accurate and comparative to realistic traffic conditions than static traffic assignment (STA), it is quite robust. To reduce computational effort and make modeling intersections more tractable, a conflict region model was used in which we discretized the intersections into conflict regions with associated capacity. The conflict region model developed under this work allows for arbitrary policies for vehicle ordering into the intersection. For example, when testing other intersection control such as first-come-first-serve (FCFS) policy (discussed later in this report), the conflict region model was integral in modeling a tractable FCFS policy. Finally, as presented in this report, the team used the previously mentioned flow and travel demand link-based mesoscopic (mid-scale) models to simulate and model CAVs and to find their effects on congestion and travel times. This allowed the team to analyze the effects of CAVs compared to a control specimen of human driven vehicles (HVs). Within analyzing the effects of CAVs on congestion, a FCFS policy applied to a tile-based reservation (TBR) system was simulated to explore the possibilities of traditional signal substitutes once AV market penetration potentially reaches 100%. The research team also analyzed the effects of rising CAV ownership on transit ridership, CAV repositioning trips, and total personal-vehicle demand using static traffic assignment (STA) simulations. Finally, the team analyzed how shared (and connected) autonomous vehicles (SAVs) may perform relative to privately held CAVs, and how preemptive vehicle relocation and dynamic ride-sharing options affect performance of the downtown transportation network simulated here, over a 2-hour morning-peak period, where most of the tripmaking is inbound. For monitoring CAVs’ effects on traffic congestion and travel times, we simulated two smaller arterial road networks, three larger freeway networks, and one large downtown network which were all ranked as part of the top 100 most congested locations and corridors within the state of Texas, so that the results would be widely applicable (TxDOT, 2015). As previously mentioned, the mesoscopic simulation used DTA with a multi-class CTM and a conflict region model to obtain metrics of total system travel time (TSTT) and time traveled per vehicle. Experiments consisted of simulating varying demands and varying proportions of AVs to HVs with traditional signals, and then running simulations using 100% AVs with a FCFS tile-based reservation (TBR) system and varying demands. Within our simulator, differences between AVs and HVs were highlighted by assuming and applying a reaction time of 1 second for HVs and 0.5 seconds for AVs. As reaction time decreases, both the capacity and backwards wave speed increase. The car-following model predicts a triangular fundamental diagram, between flow (on the y-axis) and traffic density (on the x-axis). Vehicle speed is bounded by the free-flow speed in the uncongested regime. In the congested region, speed is limited by vehicle density. After running many simulations on different networks with different demands and AV proportions, the team observed that increasing the proportion of CAVs always reduced vehicle travel time if one assumes that CAVs’ faster reaction times (vs. HVs) reduces their car-following headways, thus increasing lane capacities and signal-phase capacities naturally. While reduced headways are a reasonable expectation for advanced stages of CAV adoption, in the early stages, due to either cultural norms or caution on behalf of manufactures, there may be no reduction in headway due to CAVs. The team also found that the FCFS reservations performed worse than traditional signals for some networks, especially in freeway networks and closely packed arterial networks. At high levels of demand, reservations do not allocate capacity as efficiently as signals or provide progression across upstream and downstream signals, resulting in queue spillback along arterials. Although some exceptions to the FCFS TBR system improving traffic congestion and decreasing travel times presented themselves during simulations, FCFS did especially well on the large scale downtown Austin network, resulting in a nearly 78% reduction in travel time across the network (with 100% AVs with reduced reaction times). The reason for such a drastic decrease in travel times using TBR compared to some arterial network exceptions is that congested intersections might be avoided by dynamic user equilibrium route choice decisions. The team also used STA simulations to observe the effects of having more classes of CAV users with different values of travel time (VOTTs) and to see if there is any change in demand for these trips. It was observed that, as more travelers gain access to CAVs, the travel time (or “cost”) per trip generally falls. It was also observed that transit demand and parking demand both fall as more travelers can avoid parking costs through repositioning in SAVs and CAVs, also allowing for the reallocation of downtown parking space. This research report analyzes the potential benefits and impacts of smart driving technologies consisting of CAVs and SAVs within our current transportation networks relating to transport safety, mobility and environment. The report also shows the methodology behind models and simulations used to represent and predict such technologies. (Author/publisher)
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