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Small to medium sized transportation and logistics companies are usually constrained by limited computing and IT professional resources on implementing an efficient parallel metaheuristic algorithm for planning or management solutions. In this paper we extend the standard meta-description for genetic algorithms (GA) with a simple non-trivial parallel implementation. Our parallel GA framework is chiefly concerned with the development of a straightforward way for engineers to modify existing genetic algorithm implementations for real transportation and logistics problems to make use of commonly available hardware resources without completely reworking complex, useful and usable codes. The framework presented at its parallel base is a modification of the primitive parallelization concept, but if implemented as described it may be gradually extended to fit the qualities of any underlying problem better (via the adaptation of the merging and communications functions).We present our framework and computational results for a classical transportation related combinatorial optimization problem – the traveling salesman problem with a standard sequential genetic algorithm implementation. Our empirical analysis shows that this simple extension can lead to considerable solution improvements. We also tested our assumptions that the framework is easily implemented by an engineer not initially familiar with genetic algorithms to implement the framework for another minimum multiprocessor scheduling problem. These case studies verify that our framework is better than primitive parallelization because it gives empirically better results under equitable conditions. It also outperforms fine grained parallelization as it is easier and faster to implement.

Automatic vehicle identification (AVI) data, Integrated Circuit (IC) card data and Global Positioning System (GPS) data offer an emerging and promising source of information for analysis of traffic problems. Research on insights and information from AVI data for transport analysis has made little progress in developing specific applications especially. The emergence of multi-source data provides us with a new perspective for multi-mode transportation. This paper proposes a multi-mode traffic demand forecasting method based on AVI data, metro IC card data, and taxi GPS data. The paper extracts traffic origins and destinations (OD) information of travelers from the multi-source data and uses the extracted data for traffic zone division. Finally, a multi-mode traffic forecasting model is established on this basis. GPS data of taxi trips are selected as the clustering data and k-means algorithm was adopted to divide traffic zones in Shenzhen. Moreover, the research applies the principle of convex hull to outline the boundary of the cell. Additionally, this paper establishes the multi-mode transportation forecasting model by integrating the correlation between various transportation modes into the deep learning model for prediction. The results show that the multi-mode demand forecasting model has higher accuracy and better forecasting results comparing it with the single-mode demand forecasting model which is referring to the conventional four-step procedure. The result demonstrates that effective traffic and travel data can be obtained from multi-source data, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning. Furthermore, the substantive contribution of this research is that it provides strong empirical evidence for the existence of correlation among multi-mode travels and travel demand.

Origin Destination Matrices (ODM) are standard indicators of population migration used for urban mobility assessment. They represent a population's travel demand (number of trips between two zones) and are considered as the step two of a four-step model of transportation planning process. The research community has already recognized that ODM obtained from different sources might differ and have proposed several assessment methods in order to determine the difference among them. Besides a literature review, this research will provide a methodology for the analysis of similarity of ODMs generated from telecom big data sets compared to those obtained using other measurements. To test and validate the proposed methodology, ODM has been created using telecom big data analytics, where a significant number of anonymized telecom user locations have been analyzed to determine a travel demand. The travel demand characteristic for the same area and the same period of time has in parallel been determined/measured using “the traditional” methodology resulting in the second data set. The paper will provide the analysis results of the comparison of two resulting matrices, lessons learnt, and recommendations that can significantly improve the result correlation rate. Finally, the research has proved that a significant correlation between the resulting matrixes can be achieved if the appropriate preconditions are met.

Utilizing smartphone sensor data from crowdsen-sing (CS) campaigns for transportation planning (TP) requires highly reliable transport mode recognition. To address this, we present our RNN-based AI model MovDeep, which works on GPS, accelerometer, magnetometer and gyroscope data. It was trained on 92 hours of labeled data. MovDeep predicts six transportation modes (TM) on one second time windows. A novel postprocessing further improves the prediction results. We present a validation methodology (VM), which simulates unknown context, to get a more realistic estimation of the real-world performance (RWP). We explain why existing work shows overestimated prediction qualities, when they would be used on CS data and why their results are not comparable with each other. With the introduced VM, MovDeep still achieves 99.3 % $F_{1}$ -Score on six TM. We confirm the very good RWP for our model on unknown context with the Sussex-Huawei Locomotion data set. For future model comparison, both publicly available data sets can be used with our VM. In the end, we compare MovDeep to a deterministic approach as a baseline for an average performing model (82 - 88 % RWP Recall) on a CS data set of 540 k tracks, to show the significant negative impact of even small prediction errors on TP.

Understanding the distribution of people's transportation mode is a crucial facet of today's urban mobility for proper transportation planning. The penetration of smart-phones combined with their sensing capability is an enabler for crowdsourcing large mobility data such as commuters' GPS records. In this paper, we leverage the G PS traces of commuters to infer five different transportation modes frequently used in urban areas including foot, bike, bus, car and metro. We compare three different approaches commonly reported in the literature for transportation mode detection from the family of machine learning algorithms (random forest -RF) and deep learning architectures (convolutional neural network -CNN and ensemble of autoencoders -EAE). By splitting the dataset into train-test by the period of data collection, as well as the conventional 80–20 split, we evaluate the impact of several data pre-processing decisions on overall classifiers' performance. Our results show RF and CNN performing better upon evaluation on classification metrics such as the f1 score and the area under the Receiver Operating Characteristics (ROC) curve.

Fast delivery, high efficiency, and precision have become a new goal of the logistics industry. Many companies want to use smart transportation systems. Mobile apps, wireless communication, bar code, and other technologies are extensively employed in warehouse management as a result of the continual development and use of information technology in the logistics industry. The purpose of this paper is to implement a system to manage the warehouse and planning transportation for wheat flour in order to make the current system more functional and well-structured by using optimization algorithms to improve the working process. To get the requirement of the system, the author collects relevant data from users through interviews and observations, then analyzes and designs using UML. The system had been tested by the employee such as IT staff, Logistic staff, Warehousing staff, and Quality control staff. After a trial phase and review, the new system is successful and meets the needs of the customers, eliminates duplicate entry, and makes warehouse management efficient.

Public-transit systems face a number of operational challenges: (a) changing ridership patterns requiring optimization of fixed line services, (b) optimizing vehicle-to-trip assignments to reduce maintenance and operation codes, and (c) ensuring equitable and fair coverage to areas with low ridership. Optimizing these objectives presents a hard computational problem due to the size and complexity of the decision space. State-of-the-art methods formulate these problems as variants of the vehicle routing problem and use data-driven heuristics for optimizing the procedures. However, the evaluation and training of these algorithms require large datasets that provide realistic coverage of various operational uncertainties. This paper presents a dynamic simulation platform, called Transit-Gym, that can bridge this gap by providing the ability to simulate scenarios, focusing on variation of demand models, variations of route networks, and variations of vehicle-to-trip assignments. The central contribution of this work is a domain-specific language and associated experimentation tool-chain and infrastructure to enable subject-matter experts to intuitively specify, simulate, and analyze large-scale transit scenarios and their parametric variations. Of particular significance is an integrated microscopic energy consumption model that also helps to analyze the energy cost of various transit decisions made by the transportation agency of a city., Both Rongze Gui and Ruixiao Sun contributed to the paper equally

Coordinated development of the Beijing-Tianjin-Hebei Region is a national strategy of China, while the most import aspect among the strategy is transportation integration. By using GF-1, ZY-3 satellite data, this paper produced the latest traffic infrastructure dataset, constructed the evaluation system of transportation superiority degree through the technical indicators such as the distribution of transportation infrastructure, the density of traffic network, the influence degree of traffic trunk line, and the location superiority degree. The results show that the transportation superiority degree in Beijing-Tianjin-Hebei Region presents a “bias normal” distribution characteristic. 14.8% of the county-level administrative units have outstanding transportation advantages, with better economic development. Near 9.4% of the county-level administrative units are at an obvious disadvantage in terms of traffic conditions, and about 75.7% units are at the middle or above the middle level of the evaluation samples. The results could be introduced to the transportation planning and supervision of this region.

This article presents the supply chain management applications in economics but especially in the transportation industry. The methodology proposed in this article is based on the use of ledger technology for the integration of transport sensor systems. Ledger technology integration methods are applied, and their results contribute to measuring performance in traffic management. Centered on the development of the processes realized by the authors, a methodology - was developed within the Department of Intelligent Transport Systems of UPB, for the integration of sensors from ITS.The paper offers a concrete solution obtained at the department level. This solution based on the implementation of the ledger technology algorithm represents a new approach for monitoring and transport planning in accordance with ITS requirements.The practical aspects approached by the authors are related to the identification of the obstacles encountered in the implementation of urban transport planning management using ledger technology.Methodology based on the supply chain management algorithms approach for monitoring and predicting traffic planning using IoT networks in ITS.

Traffic volume information is widely used in all aspects of Intelligent Transportation Systems (ITS), such as transportation planning, traffic states identification, traffic management, safety analysis, and so on. In recent years, acoustic sensors are gradually applied to the detection of various traffic parameters. In this paper, acoustic data sets acquired from acoustic sensors are utilized to estimate the road traffic volume. The short-term energy (STE) algorithm and the energy to zero crossing rate (EZCR) algorithm are usually applied to acoustic analysis, and they both perform well under some simple circumstances, however, some urgent problems remain unresolved under certain complex conditions. One of such issues occurs when adjacent vehicle-pass signals (VPSs) intersect partially, seriously decreasing the accuracy of endpoint detection of VPSs, hampering the algorithm ability to maintain satisfactory traffic volume estimation. Another difficulty arises while multiple lanes are considered: some special VPSs cannot be detected. To solve these problems, a novel acoustic characteristic is defined, and an acoustic-based characteristic extraction algorithm for traffic volume estimation, entitled triangular wave analysis (TWA), is proposed. Comparing the TWA algorithm with the STE and EZCR algorithms, experimental results demonstrate the viability of the proposed algorithm in the case of intersectant VPSs.

This paper aims to propose a new analytical method of deriving the enveloping macroscopic fundamental diagram (MFD) in the light of the well-defined sufficient condition and the route choice criterion in a general network. The enveloping MFD is defined to describe the boundary of all scatter points relating the flow rate (vehicles per unit time) to the vehicle accumulation (vehicles) in a road network. The theoretical framework consists of two parts. The first part is a static congested traffic assignment model which is based on the congested link performance function and the deterministic user equilibrium principle under the entirely congested condition. The second part is the new analytical method which is proposed based on the static uncongested and congested traffic assignments with the deterministic user equilibrium, in which the well-defined sufficient condition is satisfied by employing the static traffic assignment models, and the route choice criterion is fulfilled by the deterministic user equilibrium condition (Wardrop's principle). The main findings of this paper are summarized as follows: 1) the enveloping MFD delimits a region where all scatter points are located; 2) the existence and reproducibility of the enveloping MFD in a general network are verified through numerical examples; 3) the proposed method is suggested to be applied to transportation planning for describing and evaluating the macro-characteristics and the performance of road networks and evaluating whether the OD pattern is congruous with the network topology.

Traffic flow information is of great importance for transport planning and related research. The conventional methods of automated data collection, such as annual average daily traffic (AADT) data, are often restricted by limited installation, while the state-of-the-art sensing technologies (e.g., GPS) only reflect some types of traffic flow (e.g., taxi and bus). Complete coverage of traffic flow is still lacking, thus demanding a rigorous estimation model. Most studies dedicated to estimating the traffic flow of the entire road network rely on single to only a few properties of the road network and the results may not be promising. This paper presents an idea of integrating five topological measures and road length to estimate traffic flow based on a multiple regression approach. An empirical study in Hong Kong has been conducted with three types of traffic datasets, namely floating car, public transport route, and AADT. Six measures, namely degree, betweenness, closeness, page rank, clustering coefficient, and road length, are used for traffic flow estimation. It is found that each measure correlates differently for the three types of traffic data. Multiple regression approach is then conducted, including multiple linear regression and random forest. The results show that a combination of various topological and geometrical measures has proved to have a better performance in estimating traffic flow than that of a single measure. This paper is especially helpful for transport planners to estimate traffic flow based on correlation available but limited flow data with road network characteristics.

Fil: Miguel, Fabio M. Universidad Nacional de Río Negro, Sede Alto Valle y Valle Medio. Villa Regina; Argentina Fil: Frutos, Mariano. Universidad Nacional del Sur, Department of Engineering and IIESS-CONICET, Bahía Blanca; Argentina Fil: Tohmé, Fernando. Universidad Nacional del Sur, Department of Economics and INMABB-CONICET, Bahía Blanca; Argentina Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria and SIANI, Las Palmas G. C.; Spain We present an optimization procedure based on a hybrid version of an evolutionary multi-objective decision-making algorithm for its application in urban freight transportation planning problems. This tool is intended to solve the planning problems of a merchandise distribution firm that dispatches small volume fractional loads of fresh foods on daily schedules. The firm owns a network of distribution centers supplying a large number of small businesses in Buenos Aires and its surroundings. The recombination operator of the evolutionary algorithm used here has been designed specifically for this problem. It is intended to embody a strategy that takes into account constraints like temporary closeness, closeness time window and connectivity in order to improve its performance in the clustering phase. The representation allows incorporating specific information about the actual instances of the problem and uses adaptive control of the parameters in the calibration stage. The performance of the proposed optimizer was tested against the results obtained by two evolutionary algorithms, NSGA II and SPEA 2, widely used in similar problems. We use hypervolume as a measure of convergence and dispersion of Pareto fronts. The statistical analysis of the results obtained with the three algorithms uses the Wilcoxon rank sum test, which yields evidence that our procedure provides good results. Presentamos un procedimiento de optimización basado en una versión híbrida de un algoritmo evolutivo multi-objetivo de toma de decisiones, para su aplicación en problemas de planificación del transporte urbano de mercaderías. Esta herramienta está destinada a resolver los problemas de planificación de una empresa de distribución de mercaderías que despacha pequeños volúmenes de alimentos frescos a cargas fraccionadas con periodicidad diaria. La firma posee una red de centros de distribución que abastecen a una gran cantidad de pequeñas empresas en Buenos Aires y sus alrededores. El operador de recombinación del algoritmo evolutivo utilizado ha sido diseñado específicamente para este problema. Su objetivo es incorporar una estrategia que tenga en cuenta restricciones como la cercanía temporal, la proximidad a la apertura y cierre de la ventana temporal y la conectividad, para mejorar su rendimiento en la fase de clasificación. La representación utilizada permite incorporar información específica sobre las instancias reales del problema y utiliza control adaptativo de parámetros en la etapa de calibración. El rendimiento del optimizador propuesto se probó con los resultados obtenidos por dos algoritmos evolutivos, NSGA II y SPEA 2, ampliamente utilizados en problemas de este tipo. Usamos hipervolumen como medida de convergencia y dispersión de frentes de Pareto. El análisis estadístico de los resultados obtenidos con los tres algoritmos se realizó mediante la prueba de suma de rangos de Wilcoxon, dicho análisis proporciona evidencia suficiente de que nuestro procedimiento proporciona buenos resultados.

The article describes the impact of digital technologies to improve the efficiency and quality of passenger transportation on urban ground passenger transport. The impact of new requirements to the organization of the transportation process and the role of data enrichment from different automated systems to create new services for all consumers are considered. The preconditions and practical deviations influence examples in individual subsystems on complex performance indicators are considered. Transport planning role, instrumental methods of collecting information in problem solving of increasing land-based urban passenger transport efficiency is shown. It is shown that an important element of digital transformation is route spatial digital model creation of urban passenger transport, an analytical description of track of the route, which is used in process of vehicles movement operational control. The influence of instrumental methods use of obtaining information on social standards observance for transport services quality is described. Goals and objectives of dispatching control and management of routed traffic are described, the main of which are deviation operational recording of actual traffic from the planned one, preventing an increase in traffic intervals, irregular runs, runs disruptions and consequences eliminating of violations and deviations. The role of transport services social standard for population, approved by the Ministry of Transport of Russia, in the transportation implementation of passengers and luggage by automobile transport and urban land electric transport is described.

Traffic flow characteristics are of the key factors in the process of planning and management of highway facilities. In the absence of any such information in a systematic form for Erbil city, this study has been conducted as attempt to obtain the needed data on flow, speed and density and to find the relationships among these three parameters. Travel time, travel speed and delay due to traffic interaction data are also needed to evaluate the existing levels of service which is necessary for transportation planning and economic analysis, to be done in this field. This study presents a general picture of traffic flow in terms of magnitude, daily and hourly variations with directional distribution for several road sections selected in Erbil City; 30-M Ring Road (Sawaf Mosque), 30-M Ring Road (Tairawa), Kirkuk Road, Gullan Road, 100- M Ring Road and Kasnazan Road. Data collection carried out by video camera is used for speed and volume measurements during which 720 observations were collected using video camera. To develop the relationships between each pair of flow type variables, namely speed, flow and density software was applied. The range of flow was between 900 and 5600 pc/hr and space mean speed was between 9 and 89km/hr for video camera, for all locations at study site.

Starting from the hotspots of solid waste recycling and reuse, and considering the characteristics of waste transportation in eco-industrial symbiotic network, this paper takes the transportation and distribution process of waste and by-product recycling and reuse in upstream and downstream enterprise clusters as the research object. Different from the traditional transportation research which a single goal-oriented and multi-product independent, Considering model the economic cost performance and environmental performance, as well as the "transport price correlation" between various wastes, this paper constructs a multi-objective transport planning model of Interacted multi-product. Finally, on the basis of the multi-product and multi-objective model, the paper refers to the related method theory of the second generation fast non-dominated sorting genetic algorithm (NSGA-II) which based on the elite selection strategy. By carrying out calculation and result analysis on the basis of algorithm implementation, the paper verifies the effectiveness of the model and algorithm.

Every day we hear about the current state of the climate and how it is degrading very quickly. It is urgent to study ways in which people can change and improve their daily lives towards more sustainable habits. This paper studies graph-based simulation scenarios, focusing on both human and infrastructure characteristics, which could lead to a potential reduction in CO 2 emissions. We compare alternative policies with an upper-bound base scenario, in which people use their own cars for their sole use. Most of these scenarios are based on Dijkstra’s shortest path algorithm, while others serve to underlie our reflections on the feasibility of using minimal spanning trees to solve the problem. The zone selected to illustrate this study is the coastal area of Espinho, a city in Portugal. It is shown that the scenarios with carpooling starting on the cut vertices of a tree generated by linking Dijkstra’s shortest paths of each agent yield the best results. This approach leads to a reduction in carbon emissions of between 25% and 30%.

Tour traffic prediction is an important reference for the transport planning organization of capacity and transportation planning as well as for the new-built and expansion of the transport device made in accordance with the basic needs. The common ways to forecast the tour traffic demand are regression analysis, econometric modeling, time series model, artificial neural network, and gray theory from limited literature reviews. In this study, artificial neural network is used as a tool to build prediction models. This study further uses regression analysis to find the correlations between two or more variables and their associated directions and strength, and uses the regression function to predict future changes. Finally, the study makes the result comparison and assessment of the regression analysis and artificial neural network methods. The artificial neural network has more accurate prediction value than regression analysis. The study results can provide useful references for transportation organizations to develop the strategy of business operation.

Urban Air Mobility (UAM) is believed to enhance the competitiveness of public transport. However, a UAM service has not been yet implemented, therefore, a possible network load (the number of passengers sharing the airspace) is not known. We designed a UAM network in Munich and its surrounding areas and created a base scenario with a UAM service being a part of the public transportation system. We simulated one day of commuter travelling in the PTV-Visum planning software. We estimated the average of 633 passenger trips per each UAM link. Additionally, we created 2 sets of modifications, which included fare and comfort parameters. Introducing UAM had a positive impact on the Bavarian network. Our results suggested that the cost of the UAM service has a significant influence on the passenger volumes.

Interconnection operation of rail transit line has become a key means to address the long-distance and diversified travel demands in the rail transit region. The influencing mechanism of the interconnection operation on the capacity of urban rail transit network is complex. In this study, the operation of off-line train is analyzed after interconnection. The capacity is mainly impacted during the stages of transportation scheduling and rescheduling. Three types of off-line train operation modes, namely, running only one off-line train (M-1), continuously running multiple off-line trains (M-2), and operating off-line and in-line trains at a certain proportional interval (M-3), are studied. The methods to reduce the impact on capacity during transportation planning and operation scheduling are put forward. A suburban railway and an urban rail line in Chengdu-Chongqing area are selected as the research cases to verify our research conclusions. We found that the operation of off-line trains has strict requirements on the departure interval of the line. We further found that the mode M-2 is suitable for the lines with tight capacity, and the mode M-3 is suitable for off-peak periods when the transportation capacity is relatively abundant.

Following the growing trend for electric passenger cars, due to their lower environmental and climate impacts, a trend towards the electrification of public transport is emerging, particularly regarding urban buses. However, the reliability of the service offered by electric alternatives to diesel-powered buses and the costs of the related transition have not been fully explored. In this background, we simulated the transition to electric bus powertrains for two bus lines in Locarno (Southern Switzerland) and compared the currently available electric technologies and charging schemes, based on their level of operation and their overall cost. According to these simulations, "opportunity charging" schemes emerged as the most promising ones for such urban lines in Locarno. Nonetheless, in the short-term their introduction is still hindered by barriers, such as in particular: the lack of capability to guarantee the service in case of delays above three minutes, the higher cost (between 40% and 60% higher than Euro VI diesel buses, which is also hampered by cashback on diesel custom duties), the need for proper training by the transport company staff, and the time needed to install the charging stations.

As the population grows, the importance of public transportation increased rapidly as people can use them to travel within the city or town easily. Due to the immense complexity of any public rail, a rich set of information can be uncovered and mined using Social Network Analysis (SNA). In this research, SNA is used to analyse Klang Valley's rail network and its daily riders' pattern. Klang Valley's rail network serves the area of Klang Valley and Greater Kuala Lumpur with the length of 555.7KM. SNA is a type of analysis that characterizes any phenomena within entities' relationships based on their source and destination. A directed graph was constructed based on a dataset consisting of riders' travel history containing 120 nodes and 5567 edges; the nodes represent stations and the edges are daily-commuters' travel routes between various stations. Temporal analysis is then performed on the dataset to understand the travel trends of residential and central business district (CBD) stations during peak and non-peak hours. Another analysis was also done to identify the rails' network performance when handling attacks on high degree stations; its robustness is also studied. It is found that Klang Valley's rail network is highly robust with more than 500 triad connection detected, ensuring it to recover easily if either one station fails. This research helps to provide new insight into future transportation planning and rail network traffic scheduling.

The characteristics and dynamics of human mobility have vital implications in areas such as disaster management, transportation planning and infrastructure management. While aggregate mobility modeling is useful for getting a broader overview of the system, the prediction of future movements of people in urban areas is also of significance. This work investigates the individual-level mobility of Twitter users in three Australian cities using the concepts of entropy and predictability. Twitter users are distinguished on the basis of their movement patterns and two distinct groups are identified. The randomness and regularity in their movements are calculated via multiple metrics, and prediction for the most active users in these cities is also performed. The top 10% of Brisbane users have 76.6% prediction accuracy, much higher than the other cities, suggesting heterogeneity among various cities.

Under the background of vigorously promoting the development of regional railways and the integration of metropolitan areas in Chongqing, this paper makes a thorough analysis of the rail transit system and space layout in the typical international metropolitan area combining with the spatial layout of Chongqing metropolitan area and the present situation of railway system, the development strategy of Chongqing rail transit system is put forward, the level and function of each rail transit system are determined, and the necessity of regional railway construction in metropolitan area of Chongqing is clarified, then the layout scheme of metropolitan railways in Chongqing is put forward, finally, this paper gives some suggestions for the future regional railway construction.

Predicting the impact of open residential roads will have significance not only for transportation planning and economic development, but also for the enhancement of human society. Aiming at the problem of different road and community structure, this paper considers the road length, road speed limit, traffic volume, traffic lights, and other factors to improve the traffic capability. Then uses nonlinear programming to obtain the optimal solution based on BPR road impedance model. Finally, based on different residential road structures and traffic pressure, the enhancement of open residential roads is compared to find out which road structure is optimal for opening.

Flexible manufacturing systems (FMS) are used to ensure the production of several products using the same set of production tools. Transportation tasks using robots are introduced in order to increase the routing flexibility of the FMS. Several researchers are interested in the optimization of the production planning but few of them considered the transportation planning. On another hand, each optimization algorithm needs an adapted control architecture to be executed. In this paper, we propose the use of a hybrid architecture control to implement the bi-objective optimization algorithm based on Multi-Objective Genetic Algorithm (MOGA). Results showing the efficiency of the proposed approach are presented.

Private freight companies collect a significant amount of data by utilizing advanced information and communication tools. Sharing the freight data of these companies with public policy makers, would significantly improve transportation planning decisions in the public sector. Existing literature has shown various ways of how public sectors can use this private freight data in transportation planning. However, current literature lacks a framework that can be used to guide the public policy makers to identify the most suitable uses of the shared freight data that satisfy their planning needs. This paper addresses this research gap and presents a framework for identifying how the shared freight data can be used to satisfy the planning needs of different freight stakeholders. First, relevant studies are reviewed, including a detailed illustration of the different uses of the freight data. Based on the review results, different uses of freight data are identified and categorized into six categories: Potentials of freight data in research, patterns of freight movements, estimating Green House Gas (GHG) emissions from transportation activities, analyses of parking patterns, traffic safety analyses, and traffic planning. Second, a framework is suggested to help public policy makers in identifying the most suitable groups that satisfy their transportation planning needs.

Traffic stream in many countries including Nigeria is made of different vehicles having different static and dynamic characteristic. The ban of two wheelers vehicles in Abuja city center has resulted in upsurge in the use of three wheelers. There is therefore the need to estimate the passenger car unit for this city due to the change in means of transportation for effective transport planning, designing and operation analysis. In this study, the headway method was used to develop a PCU value for trucks and three-wheelers at signalized intersections in Abuja, Nigeria. The intersections were selected based on stratified random method of sampling. Data was collected using manual method. The PCU obtained from this research is 0.92 and 1.82 for three-wheeler at Apo intersection; followed PCU of 0.92 and 2.34 at Gwarimpa intersection and lastly a PCU of 0.96 and 2.09 at Games village Intersection. The research concluded on a state average PCU value of 2.08 and 0.93 for Trucks and three wheelers for planning, designing and operation analysis in Abuja. The research recommends adapting the passenger car unit values obtained for local use and design of intersections in Abuja for efficient operation of highway facilities in the city.

MATSim is the state-of-the-art open source software for agent-based transport simulation, intended for use to evaluate transportation planning models. A standard approach to use MATSim is to conduct a user survey about their day-plans of travel, from which a synthetic dataset of agents’ day-plans for an entire region is generated for transport simulation. The simulation output can be used for various evaluations, such as congestion conditions of road segments and their peak hours.This paper aims to conduct a transportation simulation on MATSim for the region of Birmingham, AL. A traditional approach based on Iterative Proportional Fitting (IPF) is not sufficient for generating a realistic synthetic population due to the small data problem: Birmingham is a small city with limited transport data statistics, and we only have a survey of 451 people for their day-plans. To tackle the small data problem, we seek the assistance of abundant open data such as US Census data, OpenStreetMap, OpenAddresses and Birmingham Business}{Alliance to complete the fine details realistically. We also utilize various data science and machine learning techniques to build models that utilize these open data to generate a realistic population. Preliminary tests demonstrate reasonable accuracy of the simulation results.

The worsening problem of traffic congestion can be attributed to the rapid growth of the country’s population, urbanization rate and industrialization development. Traffic congestion induces environmental pollution and energy waste and significant increased levels of road accidents particularly in metro which contributes to the aggravated repercussions of climate change. Local air pollution problems and effects of climate change will remain to threat the Philippines unless an innovative model for realty and transport planning is employed. The transportation sector does a crucial part in the development of the country’s economy. Cities are referred as the "engines" of country’s growth. This paper aims to address the severe lack of the use of environmentally sustainable technology transport alternatives, i.e. diesel-electric vehicle which will serve as an instrument in the national socio-economic development. This paper discusses the development of the prototypes of locally-manufactured and environmentally sustainable transport alternatives such as (1) automated guide-way transit, (2) hybrid electric road train, and (3) hybrid electric train. The advanced transport program of the Department of Science and Technology (DOST) through Metals Industry Research and Development Center developed these prototypes which are envisioned to be efficient, fast, convenient to use, safe, all Filipino-made and environment friendly transport technology that will accommodate passengers in the Philippines and would improve competitiveness to provide additional incentives and solutions targeting pressing national challenges on urban transport.

Smart urban mobility is one of the key components to build future cities. From traffic congestion to increased travel times and pollution, most of the cities around the world are losing billions of money every year due to inefficient transportation planning. This work addresses these issues, by proving solution methodologies for travel time prediction to achieve efficient traffic management. With the improvements in processing power, RAM, data storage, etc., computationally expensive techniques such as deep learning are making their way into prediction. In this paper, we present two travel time prediction techniques, long short-term memory (LSTM) and random forest (RF). A real-life travel time data set for an urban road network of New South Wales is considered to perform numerical experiments. The performance of these techniques is analyzed and compared against some of the existing supervised learning techniques. Various prediction horizons have been investigated to validate these models. It is evident from the simulation results that the LSTM and RF models demonstrate promising predictions with very low prediction errors, varying between 0.5% to 6.8%.