Estimation of Originating-Destination Trips in Yangon by Using Big Data Source

A previously developed Deep Reinforcement Learning-based Vehicle Routing (DRL-VR) algorithm aims to be used for providing the shortest Origin-Destination (OD) travel time path in dynamic traffic environment. However, several issues may still arise regarding uncertainty associated with mixed traffic conditions coexisting Automated Vehicles (AV) and Human-driven Vehicles (HV), particularly in a wide-area urban road network. To develop a robust and interoperable route guidance algorithm based on the DRL approach, this study proposes Transfer learning-based deep reinforcement Learning Algorithm for Route Guidance (TLARG). It is an extended framework on the previous approach by incorporating transfer learning scheme that enables the DRL model of TLARG to converge even in a wide-area urban road network. The TLARG is evaluated in terms of OD travel time based on diverse OD trips with different urban road networks, including narrow- and wide-area road networks. This research conducts several evaluation studies based on microscopic traffic simulation experiments. The simulation result shows that the TLARG enables the agent to complete its OD trips not only with flexible routes but also with reductions in travel time depending on given traffic situations irrespective of network type. Furthermore, it demonstrates that the robustness of the proposed approach by measuring the error of Estimated Time of Arrival (ETA) for various OD trips in different urban road networks under the mixed traffic conditions. Such findings suggest that the TLARG has great potential to enhance the punctuality of mobility service by providing robust route guidance, even in the era of coexisting AVs and HVs.

Spatial distribution of ride-hailing trip demand and its association with walkability and neighborhood characteristics

Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, either in static or dynamic models for traffic assignment. However, OD matrices are not yet directly observable, thus, the current practice consists of adjusting an initial or a priori matrix from link flow counts, speeds, travel times and other aggregate demand data. This information is provided by an existing layout of traffic counting stations, as the traditional loop detectors. The availability of new traffic measurements provided by ICT applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications. However, the efficiently strongly depends, among other factor, on the quality of the seed matrix. This paper proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator. The paper also analyzes the sensitivity of the on-line estimator with respect to the available ICT traffic measurements.

The signaling data of cellular phones, as a passively generated, real-time, wide-coverage, and low-cost data source, have been widely used in recent studies to understand human activity and model urban travel demand. However, in contrast with the Global Positioning System (GPS) data, cellular phone signaling data are sparsely distributed in time and space, which makes travel-mode inference a challenge. Recent studies presented methods of deriving users’ home and work locations, origin-destination trips, and other activities. Very few provided a complete and feasible framework for travel-mode derivation with effective validation methods. This paper provides a real-time travel-mode derivation framework using signaling data and a web-based mapping service. A trip-chain model is proposed to detect individual activity patterns and derive the trips of mobile phone users. Then, the travel mode of each trip is identified by a Fuzzy K-Means model, which is trained and validated by the point-to-point travel time from a web-based mapping service. Finally, the travel-mode shares are aggregated and scaled to the whole population of the study area. The framework is demonstrated using cellular signaling data from 1.9 million users in Shanghai, China for seven days, and citywide point-to-point travel times from a web-based mapping service for three of those seven days. Comparing the modeled travel-mode shares with travel survey data and transportation hub statistics demonstrates the plausibility and efficiency of using a large data source (mobile-trace data and web-based mapping) to accurately assess the travel modes of people in a big city using the proposed framework.

The use of law, the main tool for implementing policy, to regulate and coordinate disaster management, preparedness and response systems features in many jurisdictions. Also, international guidance on the use of effective laws and policies, which emphasize taking on board critical stakeholders, including humanitarian responders is available. Consequently, the government of Tanzania has promulgated a new disaster management law as part of a wider reform of the legal framework to regulate disaster management and response systems which seek to involve stakeholders at all stages. This article analyses the extent to which the restructured legal framework and supporting policy address humanitarian responders who provide informal assistance, often before formal government intervention. The article traces the history of disaster management policies and laws in the country in the context of the humanitarianism – disaster management nexus. It focuses on the aftermath of the earthquake that ravaged Kagera Region in Bukoba district in the northern west part of the country in September 2016. This analysis is supported with field data from Bukoba district. The findings reveal that the legal framework for regulating disaster management and response system does not reflect international and regional clarion calls for engaging informal humanitarian responders. Recommendations on how this gap can be addressed are provided, laying emphasis on incorporating the clarion calls in order to improve the country’s legal and policy framework for disaster response and management systems.

Temporary migration trips are circulatory journeys that can be associated with tourism as these trips aren’t made on a regular basis. They’re often left unmeasured due to limited data sources, and thereby missing from regional and national travel demand models. This article presents a methodology for inferring large-scale temporary migration trips from mobile phone network data or Call Detail Record (CDR), and analyzing their spatial determinants based on urban assets derived from Google Places data. As a case study, CDR of mobile phone users in Portugal was used in our study from which insightful statistical patterns of both intra and inter-district flows were observed. Analysis of spatial determinants shows that places for leisure and population density are among the top influential factors that attract inflows into the district, while places for public service such as city hall, local government office, courthouse, and cemetery are negative urban assets for attracting district’s inflows. The presented methodology and insights gained from our analysis can be useful for transport and urban planners to establish better informed travel demand modeling and urban planning strategies.

The "Residential" Effect Fallacy in Neighborhood and Health Studies: Formal Definition, Empirical Identification, and Correction.

The path most traveled: Travel demand estimation using big data resources

Modeling passenger route choices is crucial for analyzing and predicting public transportation demand. One of the most popular methods is to use probabilistic route choice (PRC) models (also known as discrete choice models in general), which have broad applications in transportation, economics, politics, and other fields. However, its performance varies depending on the characteristics of the origin–destination (OD) trip data and should be examined carefully. This paper proposes a framework for validating the PRC model on its application to urban rail transit (URT) networks containing small‐scale OD trip data. The concept of small‐scale data is defined at first for each OD pair considering the desired confidence level and the variance of route choices. Then, a travel time range (TTR)‐based method is put forward to deduce passengers’ actual route choices as a benchmark for verifying PRC models. The difference and regularity analysis between the actual route choices and the model predictions are also performed with a twofold comparison. A case study on the Nanchang metro in China shows that the actual daily passenger volumes on routes of small‐scale OD pairs diverge remarkably from the estimations of the PRC model. The PRC model’s performance is further discussed when the small‐scale OD trip data accumulate to a larger scale over multiple days (e.g., several months). This study reveals the inherent limitation of PRC models in estimating the travel behaviors of passengers in a small‐scale population. Several practical implications are discussed to improve the route choice model and passenger flow analysis.

City metro network expansion, included in the transportation network design, aims to design new lines based on the existing metro network. Existing methods in the field of transportation network design either (i) can hardly formulate this problem efficiently, (ii) depend on expert guidance to produce solutions, or (iii) appeal to problem-specific heuristics which are difficult to design. To address these limitations, we propose a reinforcement learning based method for the city metro network expansion problem. In this method, we formulate the metro line expansion as a Markov decision process (MDP), which characterizes the problem as a process of sequential station selection. Then, we train an actor-critic model to design the next metro line on the basis of the existing metro network. The actor is an encoder-decoder network with an attention mechanism to generate the parameterized policy which is used to select the stations. The critic estimates the expected cumulative reward to assist the training of the actor by reducing training variance. The proposed method does not require expert guidance during design, since the learning procedure only relies on the reward calculation to tune the policy for better station selection. Also, it avoids the difficulty of heuristics designing by the policy formalizing the station selection. Considering origin-destination (OD) trips and social equity, we expand the current metro network in Xi'an, China, based on the real mobility information of 24,770,715 mobile phone users in the whole city. The results demonstrate the advantages of our method compared with existing approaches.

Estimation of origin-destination matrices from link traffic counts considering the interaction of the traffic modes

Estimation of origin-destination matrices from traffic counts using multiobjective programming formulations

Transit flows between stations are typically estimated indirectly using fare collecting data rather than through direct measurement. Traditional methods approximate transit flows by adapting Origin-Destination (OD) trip estimation techniques. However, these approaches have two significant limitations. First, transit link flows represent the number of passengers remaining within the transit vehicles between stations, while OD flows specifically represent passengers entering at one station and exiting at another station. Second, traditional methods rely on the assumption that a cost function is necessary without providing mathematical justification. Consequently, there lacks a robust theoretical foundation explicitly tailored for transit flow estimation. This paper addresses this gap by developing an axiomatic framework based on the Ideal Flow Network. Through systematic mathematical derivations, we identify key balance conditions and necessary constraints to achieve more accurate transit flow estimation.

Since the last decade mobile phones have changed people's lives. Mobile phone data can be utilized to derive the spatio-temporal data of subscriptions' whereabouts. It has been possible to study the mobility and traffic estimation with applications ranging from disaster management to disease epidemiology. In this work, we have focused on the use of Call Detail Records (CDRs) to explore and interpret patterns embedded in interaction flows of people through their mobile phone calls. To do so, we consider the geographical context of cell towers to discover structures of spatio-temporal interaction communities in Macau. We have explored the inter and intra-polygon interaction flows. The results suggest that subscriptions tend to communicate within a spatial-proximity community. Understanding such insight is essential for resource optimization in network planning and content distribution.