Travel time reliability is a fundamental factor in travel behavior. It represents the temporal uncertainty experienced by users in their movement between any two nodes in a network. The importance of the time reliability depends on the penalties incurred by the users. In road networks, travelers consider the existence of a trip travel time uncertainty in different choice situations (departure time, route, mode, and others). In this paper, a systematic review of the current state of research in travel time reliability, and more explicitly in the value of travel time reliability is presented. Moreover, a meta-analysis is performed in order to determine the reasons behind the discrepancy among the reliability estimates.

Value of Travel Time Reliability: A Review of Current Evidence

Urban Transportation Economics e um livro relativamente curto; sua concisao nao sacrifica, porem, sua abrangencia e consistencia. O proprio autor o considera mais como uma revisao do estado da arte, embora contenha proposicoes originais. A enfase do livro recai sobre o transporte individual.

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Urban transportation economics

Long-combination vehicles (LCVs) have significant potential to increase economic productivity for shippers and carriers by decreasing the number of truck trips, thus reducing costs. However, size and weight regulations, triggered by safety concerns and, in some cases, infrastructure investment concerns, have prevented large-scale adoption of such vehicles. Information on actual crash performance is needed. To this end, this work uses standard and heteroskedastic ordered probit models, along with the United States’ Large Truck Crash Causation Study, General Estimates System, and Vehicle Inventory and Use Survey data sets, to study the impact of vehicle, occupant, driver, and environmental characteristics on injury outcomes for those involved in crashes with heavy-duty trucks. Results suggest that the likelihood of fatalities and severe injury is estimated to rise with the number of trailers, but fall with the truck length and gross vehicle weight rating (GVWR). While findings suggest that fatality likelihood for two-trailer LCVs is higher than that of single-trailer non-LCVs and other trucks, controlling for exposure risk suggest that total crash costs of LCVs are lower (per vehicle-mile traveled) than those of other trucks.

Analysis of Large-Truck Crash Severity Using Heteroscedastic Ordered Probit Models

Excessive dependence on autonomous vehicles (AVs) may exacerbate traffic congestion and increase exhaust emissions in the future. The diffusion of AVs may be significantly affected by the public’s acceptance. A few factors that may affect people’s acceptance of AVs have been researched in the existing studies, one-third of which cited behavioral theories, while the rest did not. A total of seven factors with behavior theories are screened out that significantly affect the acceptance intention, including perceived ease of use, attitude, social norm, trust, perceived usefulness, perceived risk, and compatibility. Six factors without behavior theories are summed up that affect AV acceptance, namely safety, performance-to-price value, mobility, value of travel time, symbolic value, and environmentally friendly. We found that people in Europe and Asia have substantial differences in attitudes toward AVs and that safety is one of the most concerned factors of AVs by scholars and respondents. Public acceptance of the different types of AVs and consumers’ dynamic preferences for AVs are highlighted in the review too. The quality of literature is systematically assessed based on previously established instruments and tailored for the current review. The results of the assessment show potential opportunities for future research, such as the citation of behavior theories and access to longitudinal data. Additionally, the experimental methods and the utilization of mathematical and theoretical methods could be optimized.

https://www.mdpi.com/2071-1050/12/5/1719/pdf

The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

The determinants of public acceptance of autonomous vehicles: An innovation diffusion perspective

Perceptions and expectations of autonomous vehicles – A snapshot of vulnerable road user opinion

Coronavirus has had a large-scale impact on transportation This study attempts to assess the effects of COVID-19 on biking Bikeshare data was used to understand the impacts of COVID-19 during the initial wave of the disease on biking in New York City, Boston, and Chicago As the cases increased, these cities experienced a reduction in bikeshare trips, and the reductions were different in the three cities Correlations were developed between COVID-19 cases and various bikeshare related variables The study period was split into three phases—no COVID-19 phase, cases increasing phase, and cases decreasing phase—to examine how the residents of the three cities reacted during the different phases of the coronavirus spread While bike trips decreased, the average duration of the trips increased during the pandemic NYC's average trip duration was consistently less than that of Boston and Chicago, which could be due to its sprawl (NYC is considered as more compact and connected compared to the other two cities) © 2020 The Author(s)

COVID-19 effects on shared-biking in New York, Boston, and Chicago

Risk drivers pose to themselves and other drivers by violating traffic rules.

Traffic performance of a highway is expressed in terms of level of service (LOS). It is a major input to important decisions on public fund investments. Because of its unique operational characteristics, evaluation of traffic performance on two-lane highways is a critical task. This research examines the applicability of various performance measures for two-lane highways under mixed traffic conditions and assigns threshold values for different levels of service based on the best performance measure observed. Graphical and statistical analyses are carried out among the six most popular performance measures and traffic volume, but none of the considered measures are found to be reliable. A new parameter, number of followers (NF), which is defined as the number of vehicles traveling with a gap less than 2.6 s, is found to have good correlation with traffic volume but it does not reflect the true congestion conditions of a roadway. Therefore, the NF is further modified to the number of followers as a ...

Evaluation of Performance Measures for Two-Lane Intercity Highways under Mixed Traffic Conditions

In March 2018, an Uber-pedestrian crash and a Tesla's Model X crash attracted a lot of media attention because the vehicles were operating under self-driving and autopilot mode respectively at the time of the crash. This study aims to conduct before-and-after sentiment analysis to examine how these two fatal crashes have affected people's perceptions of self-driving and autonomous vehicle technology using Twitter data. Five different and relevant keywords were used to extract tweets. Over 1.7 million tweets were found within 15 days before and after the incidents with the specific keywords, which were eventually analyzed in this study. The results indicate that after the two incidents, the negative tweets on “self-driving/autonomous” technology increased by 32 percentage points (from 14% to 46%). The compound scores of “pedestrian crash”, “Uber”, and “Tesla” keywords saw a 6% decrease while “self-driving/autonomous” recorded the highest change with an 11% decrease. Before the Uber-incident, 19% of the tweets on Uber were negative and 27% were positive. With the Uber-pedestrian crash, these percentages have changed to 30% negative and 23% positive. Overall, the negativity in the tweets and the percentage of negative tweets on self-driving/autonomous technology have increased after their involvement in fatal crashes. Providing opportunities to interact with this developing technology has shown to positively influence peoples' perception.

Praveena Penmetsa

Papers

Perceptions and expectations of autonomous vehicles – A snapshot of vulnerable road user opinion

COVID-19 effects on shared-biking in New York, Boston, and Chicago

Risk drivers pose to themselves and other drivers by violating traffic rules.

Evaluation of Performance Measures for Two-Lane Intercity Highways under Mixed Traffic Conditions

Effects of the autonomous vehicle crashes on public perception of the technology