Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connected-vehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations

Assessing the emission impacts of autonomous vehicles on metropolitan freeways

While recent studies demonstrate the societal and economic benefits of driverless vehicles, little is known about the emission impacts of autonomous vehicles (AVs) in the context of mixed traffic. This paper explores the environmental impacts of AVs along an urban freeway corridor in a metropolitan area using Vehicle Specific Power (VSP) and EMEP/EEA emission methodologies paired with VISSIM traffic model. Three different AV penetration rates were implemented for through traffic along a freeway corridor in the city of Porto (Portugal) by considering long-term market predictions (10%, 20% and 30%). Afterwards, these scenarios were compared to current situation in terms of carbon dioxide, carbon monoxide, nitrogen oxides and hydrocarbon emissions, and travel time and stop-and-go situations. The emissions and traffic performance of each scenario were evaluated on three levels: a) overall study domain; b) corridor; c) impact of AVs on conventional vehicles (CVs). AVs yielded small savings in emissions in the overall study domain for automation levels below 30% (differences in traffic performance and emissions were not statistically significant). Corridor-level analysis showed decreases of 5% in emissions can be expected with AVs technology, but it penalizes travel time up to 13% for both AV and CV, when compared to the existing situation.publishe

Investigating the interaction between the parking choice and holiday travel behavior

Parking is one of the key links between the urban planning and transportation operation. However, most studies in this field focus on the parking behavior on workdays, and the holiday parking is seldom investigated. This study analyzes the interaction between the parking choice and travel behavior in the holidays. Data were collected at Fragrant Hills and Beijing Botanical Garden during the Qingming Festival (Tomb-sweeping Days) in 2013. The structural equation modelling was applied to examine the causal effects and quantitative relationships between the parking choice and holiday travel behavior and identify the main influencing factors based on the activity analysis. The results show that the parking choice has a close relationship with holiday travel behavior, which is more than an explanatory variable for the travel behavior. Moreover, the parking space availability, parking charge, and walking distance have significant effects on holiday parking choice. In addition, the personal attributes and household characteristics are significant influencing factors for the parking choice and holiday travel behavior

An Improved Multi-objective Algorithm for the Urban Transit Routing Problem

The determination of efficient routes and schedules in public transport systems is complex due to the vast search space and multi- ple constraints involved. In this paper we focus on the Urban Transit Routing Problem concerned with the physical network design of pub- lic transport systems. Historically, route planners have used their local knowledge coupled with simple guidelines to produce network designs. Several major studies have identified the need for automated tools to aid in the design and evaluation of public transport networks. We propose a new construction heuristic used to seed a multi-objective evolutionary al- gorithm. Several problem specific mutation operators are then combined with an NSGAII framework leading to improvements upon previously published results

Which service is better on a linear travel corridor: Park & ride or on-demand public bus?

This paper develops an analytical model to support the decision-making for selection of a public transport service (PTS) provision between park & ride and on-demand public bus (ODPB). The objective of the model is to maximise the total social welfare, which includes consumer surplus and operator’s net profit. The model is solved by a heuristic solution procedure and tested on an idealized linear travel corridor. The case study considers the effects from population density, density distribution, size of residential area, P&R station location, distance from the residential area to centre business area (CBD), as well as the changes of residential area layout and population growth. Results show that P&R fits for low population density area while ODPB is more suitable for high population density area. Population distribution type has little influence on the services’ social welfare. ODPB is a preferable service for the city which does not have advanced metro network. Besides, the investment time for building ODPB service in the planning horizon is discussed with consideration of the development of residential area

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Road space optimisation for multiclass and multimodal traffic networks

© 2017 Dr Saeed Asadi BaloeeTraffic congestion has become a serious concern and hindrance to the prosperity of many societies. Among a variety of solutions two approaches are of significant importance: constructing new roads and bridges to ease traffic congestion and promoting public transport. For the latter, the aim is to provide more space in the heart of cities for public transport (buses, trams, etc) aiming to get more commuters to their destinations. Therefore, two central questions have been addressed in this research; (i) investment in the road construction: given a number of candidate projects associated with construction expenses and a limited budget, what is the best choice of projects. This is known as the road network design problem (NDP), and (ii) transit priority lanes: given a road network, which roads should be selected to provide a lane to be exclusively used by public transport modes such that the overall performance of the transport system is not adversely affected. This problem is called the, “transit priority lane design problem” (TPLDP). For the former, (NDP) a hybridized method consisting of the branch and bound algorithm and Benders decomposition method has been developed. For the latter (TPLDP), the concept of Braess paradox was employed to seek for “mis-utilized” space in congested networks to be utilized by public transport. To this end, a merit index aiming to spot potentially some Braess-tainted roads is introduced first. Then a branch and bound algorithm was developed to find the best subset of the Braess tainted roads that have no adverse impact on the overall performance of the network. This study advances the state of knowledge in the above mentioned problems in five areas: (i) the authenticity of the traffic model is enhanced by subjecting all the analysis to multimodal and multiclass traffic circulation, (ii) the methodologies developed in this study are tailored to real world applications as illustrated with numerical analysis, (iii) a RAM-efficient branch and bound algorithm (BB) has been developed such that the expansion of the BB’s tree structure becomes memoryless, (iv) inclusion of the Braess paradox in the pursuit of the transit priority lane would nullify possible adverse effects on the private modes, and (v) a new method for the capacitated traffic assignment has been developed which is called inflated travel time