ALBERT

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 97〈/p〉 〈p〉Author(s): Alexandros Angelopoulos, Damianos Gavalas, Charalampos Konstantopoulos, Damianos Kypriadis, Grammati Pantziou〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The asymmetric demand-offer problem represents a major challenge for one-way vehicle sharing systems (VSS) affecting their economic viability as it necessitates the engagement of considerable human (and financial) resources in relocating vehicles to satisfy customer demand. In this paper, we propose a novel approach which involves user-based vehicle relocations to address supply-and-demand mismatches; in our approach, VSS users are offered price incentives so as to accept picking up their vehicle from an oversupplied station and/or to drop it off to an under-supplied station. The system incentivizes users based on the priorities of vehicle relocations among stations, taking into account the fluctuating demand for vehicles and parking places at different stations over time. A graph-theoretic approach is employed for modeling the problem of allocating vehicles to users in a way that maximizes the profit of the system taking into account the budget the VSS can afford to spend for rewarding users, as well as the users’ strategic behavior. We present two different schemes for incentivizing users to act in favour of the system. Both schemes consider budget constraints and are truthful and budget-feasible. We have extensively evaluated our approach through simulations which demonstrated significant gain with respect to the number of completed trips and system revenue. We have also validated our approach through pilot trials conducted in a free-floating e-motorbike sharing system in the framework of an EU-funded research project.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 97〈/p〉 〈p〉Author(s): Jian Wen, Yu Xin Chen, Neema Nassir, Jinhua Zhao〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Autonomous vehicles (AVs) represent potentially disruptive and innovative changes to public transportation (PT) systems. However, the exact interplay between AV and PT is understudied in existing research. This paper proposes a systematic approach to the design, simulation, and evaluation of integrated autonomous vehicle and public transportation (AV + PT) systems. Two features distinguish this research from the state of the art in the literature: the first is the transit-oriented AV operation with the purpose of supporting existing PT modes; the second is the explicit modeling of the interaction between demand and supply.〈/p〉 〈p〉We highlight the transit-orientation by identifying the synergistic opportunities between AV and PT, which makes AVs more acceptable to all the stakeholders and respects the social-purpose considerations such as maintaining service availability and ensuring equity. Specifically, AV is designed to serve first-mile connections to rail stations and provide efficient shared mobility in low-density suburban areas. The interaction between demand and supply is modeled using a set of system dynamics equations and solved as a fixed-point problem through an iterative simulation procedure. We develop an agent-based simulation platform of service and a discrete choice model of demand as two subproblems. Using a feedback loop between supply and demand, we capture the interaction between the decisions of the service operator and those of the travelers and model the choices of both parties. Considering uncertainties in demand prediction and stochasticity in simulation, we also evaluate the robustness of our fixed-point solution and demonstrate the convergence of the proposed method empirically.〈/p〉 〈p〉We test our approach in a major European city, simulating scenarios with various fleet sizes, vehicle capacities, fare schemes, and hailing strategies such as in-advance requests. Scenarios are evaluated from the perspectives of passengers, AV operators, PT operators, and urban mobility system. Results show the trade off between the level of service and the operational cost, providing insight for fleet sizing to reach the optimal balance. Our simulated experiments show that encouraging ride-sharing, allowing in-advance requests, and combining fare with transit help enable service integration and encourage sustainable travel. Both the transit-oriented AV operation and the demand-supply interaction are essential components for defining and assessing the roles of the AV technology in our future transportation systems, especially those with ample and robust transit networks.〈/p〉 〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 97〈/p〉 〈p〉Author(s): Yong-Xian Huang, Rui Jiang, HM Zhang, Mao-Bin Hu, Jun-Fang Tian, Bin Jia, Zi-You Gao〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉To investigate the car-following behavior under high speed driving conditions, we performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 97〈/p〉 〈p〉Author(s): Lu Zhen, Hongtao Hu, Wencheng Wang, Xin Shi, Chengle Ma〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper investigates crane scheduling problems for a new type of automated container terminal system, which is based on multi-storey frame bridges. For the new design concept, this paper studies how to schedule two types of cranes, i.e., quay cranes and bridge cranes that transfer containers between different storeys. The schedules of these two types of cranes impact the operation efficiency of the terminal system. Mathematical models are proposed for the two scheduling problems. Meta-heuristics are developed to solve them. Numerical experiments are conducted to validate the effectiveness of the proposed models and the efficiency of the proposed solution method.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Seung Hoon Cheon, Changju Lee, Seongil Shin〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In modern urban transit networks, buses and subways are not distinguished as different modes of transportation; this makes it challenging to analyze travel behaviors with multiple modes for the purpose of developing policies and plans. With the introduction of Automatic Fare Collection (AFC) systems, these modes are operated along a complex of links and nodes that constitute a multimodal transit network. Methods for analyzing travel behaviors in mass transit have been developed, but previous approaches fail to adequately reflect travel behaviors and network features (e.g., transfers, mode and route preferences). To overcome such limitations, this research proposes a smart card data-based analytical method with which travel behaviors can be efficiently and accurately examined. AFC systems provide a tremendous amount of data that contain detailed trip information, and using these data reinforces the reliability of the proposed data-driven method. The proposed method of analysis involves four core processes: establishing a scheme for how multiple transit modes can be integrated into one multimodal transit network on the basis of information derived from the AFC system, selecting feasible paths, assigning trips using a stochastic approach, and verifying analytical results by comparing them with findings from trip datasets. This method was used to analyze monthly smart card data collected from the AFC system in 2009 in the greater Seoul area. Multimodal transit networks were constructed from 34,852 bus stops and 539 subway stations using smart card data, and in total, 3,614,875 trips were used in the analysis. The final model for stochastic transit assignment was developed using the proposed method, which was verified by comparing actual and assigned trips. The proposed method exhibits high accuracy (83.93%) and a high R-square value (0.981), which supports the strength of the proposed stochastic transit assignment model. The findings reveal new interesting research directions for exploration, such as developing more disaggregated models (e.g., for specific regions, times, and users), considering detailed transfer features (e.g., transferable boundaries, transfer facilities, and transfer times), confirming the method’s applicability by testing it in other cities, and incorporating both multimodal transit and road networks into the proposed model.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Yedidya Levi, Shlomo Bekhor, Yehiel Rosenfeld〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉This paper develops an optimization model for planning an artificial island composed of Very Large Floating Structures (VLFSs). The optimization model addresses the geometric shape of the island (the array of floating platforms), the land-use layout, and the transportation network. The model developed in this paper considers the specific properties related to an artificial island made of multiple floating modules.〈/p〉 〈p〉The model is formulated as a bi-level optimization problem. In the upper level problem, the decision-maker is required to decide the land-use layout and the road network structure. The user behavior pattern is obtained by solving the lower level problem, which is dependent on the decisions taken at the upper level. Given the complexity of the problem, the paper develops modifications to the genetic algorithm to find the Pareto front in a reasonable amount of time.〈/p〉 〈p〉A test case illustrates the feasibility of the proposed planning method. The transportation model execution is the most time-consuming task, which highlights the need to prune irrelevant scenarios. The Pareto front shows a substitution pattern between different land-use configurations, in accordance with the different objective functions.〈/p〉 〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Milos Balac, Henrik Becker, Francesco Ciari, Kay W. Axhausen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉A considerable amount of research has been devoted to understanding and modeling carsharing services in the past. However, a research question that has not been investigated yet is how carsharing companies affect each other in a competitive market. This paper presents a methodology, based on the MATSim framework, for investigating the interaction of carsharing competitors and uses it to analyze the possible competition of two free-floating carsharing companies in the city of Zurich, Switzerland. It further provides a first attempt to allow agents to make mode-choice decisions based on a discrete choice model that includes carsharing alternatives on a whole plan level. Relocation capabilities are added to the carsharing module that allow for testing of various relocation algorithms.〈/p〉 〈p〉The paper investigates the competition of carsharing operators on two levels. First, by investigating the impact of different price levels, and second, by allowing one or both of the competitors to perform relocations. The obtained results show that (1) with fixed market rate, operators can obtain the highest profit by offering the service at 0.41 CHF/min, (2) offering different pricing schemes can lead to a higher cumulative payoff, although one of the competitors is left with a reduced profit and (3) performing relocations is unprofitable for the company tasked with the burden of doing it, whilst a competitor may enjoy a free-rider effect with an increasing number of rentals and higher profit.〈/p〉 〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Wei Ni, Michael J. Cassidy〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The work explores how Reinforcement Learning can be used to re-time traffic signals around cordoned neighborhoods. An RL-based controller is developed by representing traffic states as graph-structured data and customizing corresponding neural network architectures to handle those data. The customizations enable the controller to: (i) model neighborhood-wide traffic based on directed-graph representations; (ii) use the representations to identify patterns in real-time traffic measurements; and (iii) capture those patterns to a spatial representation needed for selecting optimal cordon-metering rates. Input to the selection process also includes a total inflow to be admitted through a cordon. The rate is optimized in a separate process that is not part of the present work. Our RL-controller distributes that separately-optimized rate across the signalized street links that feed traffic through the cordon. The resulting metering rates vary from one feeder link to the next. The selection process can reoccur at short time intervals in response to changing traffic patterns. Once trained on a few cordons, the RL-controller can be deployed on cordons elsewhere in a city without additional training.〈/p〉 〈p〉This portability feature is confirmed via simulations of traffic on an idealized street network. The tests also indicate that the controller can reduce the network’s vehicle hours traveled well beyond what can be achieved via spatially-uniform cordon metering. The extra reductions in VHT are found to grow larger when traffic exhibits greater in-homogeneities over the network.〈/p〉 〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Grigoris Antoniou, Sotiris Batsakis, John Davies, Alistair Duke, Thomas L. McCluskey, Evtim Peytchev, Ilias Tachmazidis, Mauro Vallati〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Improving a city’s infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment is that this may enable the use of intelligent processes to autonomously manage urban facilities such as traffic signal controls.〈/p〉 〈p〉In this paper we document an attempt to integrate sets of sensor and historical data using a data hub and a set of ontologies for the data. We argue that access to such high level integrated data sources leads to the enhancement of the capabilities of an urban transport operator. We demonstrate this by documenting the development of a planning agent which uses such data as inputs in the form of logic statements, and when given traffic goals to achieve, outputs complex traffic signal strategies which help transport operators deal with exceptional events such as road closures or road traffic saturation. The aim is to create an autonomous agent which reacts to commands from transport operators in the face of exceptional events involving saturated roads, and creates, executes and monitors plans to deal with the effects of such events. We evaluate the intelligent agent in a region of a large urban area, under the direction of urban transport operators.〈/p〉 〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Mehdi Rafati Fard, Afshin Shariat Mohaymany〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The importance of calibration of microscopic traffic models as the main core of traffic simulation software results from the need for more realistic traffic behaviors. The latent essence of several parameters in such models as well as the uncertainties resulting from the noise in the data, make the process of calibration much more complex. Usually, the calibration process is formulated as an optimization problem. Selecting the appropriate solution algorithm due to nonlinear and non-convex nature of the problem is crucial. The importance of the issue is more significant when the matter of calibrating the medium or large-scale simulation model is considered. This is mainly due to the expensive cost that running the simulation models impose. Therefore, applying the current algorithms in which finding the appropriate solutions requires a large number of simulation runs is not deemed suitable. In this paper, an estimation of distribution algorithm based on copula theory has been suggested. In contrast with traditional solution algorithms, in the proposed algorithm complex interaction between parameters of a model has been considered by constructing and sampling from a copula-based probabilistic model. Copulas are functions that describe the dependence structure of a set of random variables and connect multivariate distribution functions to one-dimensional marginal distribution functions. The results indicate that applying an explicit probabilistic model based on copula helps the estimation of distribution algorithm to explore the search space more effectively and efficiently as well as provides the possibility of extracting the knowledge with regard to the structure of the calibration problem through analyzing the probabilistic models that are constructed during the evolution process. Furthermore, this new algorithm has been compared with the genetic algorithm and kernel-based cross-entropy method on synthetic and real trajectory data. The results confirm that the proposed algorithm is more efficient in terms of convergence rate, resource usage and more robust in terms of the probability of finding the global optimal solution.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Michael Schultz, Stefan Reitmann〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Reliable and predictable ground operations are essential for punctual air traffic movements. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. We used a recurrent neural network approach to predict the progress of a running boarding event. In particular, we implemented and trained the Long Short-Term Memory model. Since no operational data of the specific passenger behavior is available, we used a reliable, validated boarding simulation environment to provide data about the aircraft boarding events. First predictions show that uni-variate input (seat load progress) produces insufficient results, so we consider expected passenger interactions in the aircraft cabin as well. These interactions are aggregated to a prior-developed complexity metric and allow an efficient evaluation of the current boarding progress. With this multi-variate input, our Long Short-Term Memory model achieves appropriate prediction results for the boarding progress.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Christos Gkartzonikas, Konstantina Gkritza〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper provides a review of studies published in peer-reviewed journals, conference proceedings, and technical academic and private sector reports on surveys about autonomous vehicles (AVs) from 2012 onward. The studies and respective surveys are categorized in this paper based on the study objectives and methodology applied. More than half of the reviewed studies on AVs focus on capturing individuals’ behavioral characteristics and perceptions. The second most prevalent category includes studies about individuals’ willingness to pay to use AVs. The reviewed studies were also categorized according to the study population. The paper identifies and classifies attitudinal questions in each survey into different components that may affect behavioral intention to ride in AVs and provides information on specific hypotheses that were set in the studies. Moreover, a discussion of the benefits, barriers/concerns, and opportunities related to the deployment of AVs is presented. The paper concludes by summarizing the lessons learned and outlining the research gaps.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Goran Molnar, Gonçalo Homem de Almeida Correia〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Reservations in daily services can improve user satisfaction, and give additional information about the demand patterns to the operators. However, providing reservations to carsharing clients is difficult. While carsharing is especially convenient if it is allowing one-way trips and vehicle drop-off anywhere in the service area (called free-floating), this flexibility increases management complexity because of vehicle stock imbalance. Most of the commercial providers of free-floating carsharing offer reservations under highly restrictive terms, for example only up to 30 min in advance. In this paper, we propose an innovative reservation enforcement technique that allows substantially longer reservation times while keeping the system profitable and achieving high service quality. A simple way to enforce reservations is locking vehicles until the departure time of a client. However, it comes at the cost of idling vehicles that could be used by other users and decreasing the revenue. Our approach, called relocations-based reservation enforcement method (R-BR) combines vehicle locking and relocation movements. It locks vehicles only a short time before the trip departure if a suitable vehicle is close enough due to the natural trip patterns. If no such vehicle is available, a car is relocated from another place. Further, we propose a variable quality of service (QoS) model in which the guaranteed radius around the user within which the reserved vehicle will be placed, and the maximum allowed reservation time before the departure depends on the zone of trip departure. A simulation-based optimization is used whereby the carsharing operation is simulated and optimized using an iterated local search (ILS) metaheuristic for adjustment of service level parameters. The proposed technique is tested on a set of artificial problem examples and a case study of a simulated working day in the Lisbon Municipality, Portugal. Results show that the proposed R-BR method is substantially better than the simple vehicle locking when the constant QoS approach is used and that the devised ILS metaheuristic can further increase the system performance, especially with high trip volumes.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Menno Yap, Ding Luo, Oded Cats, Niels van Oort, Serge Hoogendoorn〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Minimizing passenger transfer times through public transport (PT) transfer synchronization is important during tactical planning and real-time control. However, there are computational challenges for solving this Timetable Synchronization Problem (TSP) for large, real-world urban PT networks. Hence, in this study we propose a data-driven, passenger-oriented methodology as a preparatory selection stage to reduce problem dimensionality by (1) determining the significant transfer hubs in the network, and (2) identifying subsets of lines within these hubs that need to be prioritized for transfer synchronization. In the first phase of our methodology we determine the spatial boundaries of transfer locations, using a clustering technique based on the passenger transfer flow matrix inferred from smartcard data. After that, a subset of hubs to be prioritized for synchronization is selected. In the second phase, we characterize the transfer patterns within the hubs based on a topological representation. Based on these topological graphs, the line bundles that need to be prioritized within the hubs are further identified using a modularity-based community detection technique. We apply our methodology to a real-world case study, i.e. the PT network of The Hague, the Netherlands. For this case study, our approach allows for prioritizing 70% of all transfers within identified transfer locations while only requiring 0.9% of these transfer locations, thus reducing the complexity of solving the TSP substantially at a relatively low cost. Our method supports public transport operators during timetable design and real-time control in determining where and which lines to prioritize when devising measures for improving transfer experience and synchronization.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): M.N. Sharath, Nagendra R. Velaga, Mohammed A. Quddus〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Existing map-Matching (MM) algorithms primarily localize positioning fixes along the centerline of a road and have largely ignored road width as an input. Consequently, vehicle lane-level localization, which is essential for stringent Intelligent Transport System (ITS) applications, seems difficult to accomplish, especially with the positioning data from low-cost GPS sensors. This paper aims to address this limitation by developing a new dynamic two-dimensional (D2D) weight-based MM algorithm incorporating d〈em〉ynamic weight coefficients〈/em〉 and 〈em〉road width〈/em〉. To enable vehicle lane-level localization, a road segment is virtually expressed as a matrix of homogeneous grids with reference to a road centerline. These grids are then used to map-match positioning fixes as opposed to matching on a road centerline as carried out in traditional MM algorithms. In this developed algorithm, vehicle location identification on a road segment is based on the total weight score which is a function of four different weights: (i) proximity, (ii) kinematic, (iii) turn-intent prediction, and (iv) connectivity. Different parameters representing network complexity and positioning quality are used to assign the relative importance to different weight scores by employing an 〈em〉adaptive regression method〈/em〉. To demonstrate the 〈em〉transferability〈/em〉 of the developed algorithm, it was tested by using 5,830 GPS positioning points collected in Nottingham, UK and 7,414 GPS positioning points collected in Mumbai and Pune, India. The developed algorithm, using stand-alone GPS position fixes, identifies the correct links 96.1% (for the Nottingham data) and 98.4% (for the Mumbai-Pune data) of the time. In terms of the correct lane identification, the algorithm was found to provide the accurate matching for 84% (Nottingham) and 79% (Mumbai-Pune) of the fixes obtained by stand-alone GPS. Using the same methodology adopted in this study, the accuracy of the lane identification could further be enhanced if the localization data from additional sensors (e.g. gyroscope) are utilized. ITS industry and vehicle manufacturers can implement this D2D map-matching algorithm for liability critical and in-vehicle information systems and services such as advanced driver assistant systems (ADAS).〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Ji Ma, Daniel Delahaye, Mohammed Sbihi, Paolo Scala, Miguel Antonio Mujica Mota〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 97〈/p〉 〈p〉Author(s): 〈/p〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Tingru Zhang, Da Tao, Xingda Qu, Xiaoyan Zhang, Rui Lin, Wei Zhang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The purpose of this study was to explore factors affecting users’ acceptance of automated vehicles (AVs, Level 3). A theoretical acceptance model was proposed by extending the Technology Acceptance Model (TAM) with new constructs: initial trust and two types of perceived risk (i.e., perceived safety risk [PSR] and perceived privacy risk [PPR]). It was hypothesized that initial trust was built upon perception factors (i.e., perceived usefulness [PU], perceived ease of use [PEOU], PSR, and PPR) and was a key determinant of AV acceptance. The validity of the model was confirmed with a structure equation modeling analysis based on data collected from 216 survey samples. Results revealed that initial trust was the most critical factor in promoting a positive attitude towards AVs, which, together with PU, determined users’ intention to use AVs. Initial trust could be enhanced by improving PU and reducing PSR associated with AVs. Theoretically, these findings suggest that initial trust offers another and probably more important pathway for other factors to impact consumers’ adoption of systems with uncertainty. Practically, the findings provide guidance for designing interventions aimed at improving public’s acceptance towards AVs.〈/p〉〈/div〉 〈/div〉

Beschreibung: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Transportation Research Part C: Emerging Technologies, Volume 98〈/p〉 〈p〉Author(s): Sander van Cranenburgh, Ahmad Alwosheel〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study develops a novel Artificial Neural Network (ANN) based approach to investigate decision rule heterogeneity amongst travellers. This complements earlier work on decision rule heterogeneity based on Latent Class discrete choice models. We train our ANN to recognise the choice patterns of four distinct decision rules: Random Utility Maximisation, Random Regret Minimisation, Lexicographic, and Random. Next, we apply our trained ANN to classify the respondents from a recent Value-of-Time Stated Choice experiment in terms of their most likely employed decision rule. We cross-validate our findings by comparing our results with those from: (1) single class discrete choice models estimated on subsets of the data, and (2) latent class discrete choice models. The cross-validations provide strong support for the notion that ANNs can be used to identify underlying decision rules in choice data. As such, we believe that ANNs provide a valuable addition to the toolbox of analysts who wish to investigate decision rule heterogeneity. The substantive contribution of this study is that we provide strong empirical evidence for the presence of decision rule heterogeneity amongst travellers.〈/p〉〈/div〉 〈/div〉