Traffic Impact Assessment, Diversion and Management Plan for Surat Metro Line 1, Package –CS3, Surat

Package CS3 of Metro Line 1 in Surat City includes 3 stations i.e. Surat railway station, Maskati Hospital and Chowk Bazar. As per proposed Metro plan of Surat city, this line will originate from Sarthana station and will terminate at Dream City. The length of Line 1 is 21.61KM of which 14.59km is elevated whereas 7.02km is Underground and consists of 20 Stations. This metro line envisages use of public transport system in Surat city and shall cater the present and future travel demand of the catchment area and shall also reduce load from road based transport system of the corridor. During the construction phase of any Mass Rapid Transit System (MRTS) running along the Right of Way (ROW) of existing roadway system, Traffic diversion and management plan implementation becomes absolute mandatory to reduce congestion, conflicts increase level of safety and ease construction process. Similarly, for package CS3 of Surat Metro line 1, there is need of preparing an implementing Traffic Diversion and Management plan to create a synergy amongst construction activities, traffic flow, safety of pedestrian and construction worker with minimal impact on surrounding catchment. This study shall provide Traffic diversion and management plan which may help to cater the existing traffic and stir them in a smooth and non-congested flow with the help of signage’s, road markings, etc.

THE PROBLEM OF OPTIMIZING BUS ROUTE USING GEOINFORMATION ENVIRONMENT

The public transport system is characterized by a complex transport network, intersecting routes and fragmented stopping points. In this regard, the functioning of the transport system to ensure optimal planning of bus transportation is an urgent task. A solution to this problem can be a multi-level model that allows you to represent network objects at several spatial levels, calculate the exact time delay, and meet the needs of planning bus trips with various optimization criteria

Using weighted multilayer networks to uncover scaling of public transport system

The public transport system is considered as one of the most important subsystems in metropolises for achieving sustainability objectives by mediating resources and travel demand. Representing the various urban transport networks is crucial in understanding travel behavior and the function of the transport system. However, previous studies have ignored the coupling relationships between multi-mode transport networks and travel flows. To address this problem, we constructed a multilayer network to illustrate two modes of transport (bus and metro) by assigning weights of travel flow and efficiency. We explored the scaling of the public transport system to validate the multilayer network and offered new visions for transportation improvements by considering population. The proposed methodology was demonstrated by using public transport datasets of Shanghai, China. For both the bus network and multilayer network, the scaling of node degree versus Population were explored at 1 km * 1 km urban cells. The results suggested that in the multilayer network, the scaling relations between node degree and population can provide valuable insights into quantifying the integration between the public transport system and urban land use, which will benefit sustainable improvements to cities.

Assessing the impact of Transportation on Climate Change

India's North-East Region has greater demand for road and personalized modes of transport powered by fossil fuels. And due to emissions, there has been evidence of climate change. It has been found that diesel cars cause greater emissions (per kilometer travelled) as compared to petrol cars; therefore, the use of diesel should be discouraged. The chapter suggests that the emissions in case of public transport passenger-km are lesser than other modes of transport. However, in the North-East Region, there is negligible share of public transport due to poor infrastructure and service facilities. Therefore, improvements should be made with regard to public transport system so that considerable number of passengers shifts to public transport modes. Further, it is inferred that use of alternate vehicle or fuel technologies like hybrid electric vehicles, biofuel, biodiesel, hydrogen fuel need to be initiated to mitigate the climate change.

Combined optimisation and MCDA based solution of the tram depot location problem

This paper deals with an issue of technical facilities location in a public transport system. The decision problem is formulated as a selection of the most advantageous alternative, i.e. the location of a new tram depot among the already existing facilities of this type. The selection is preceded by the evaluation of the alternatives. The assessment is not a trivial task, because there are many groups of interest with usually contradictory points of view. Therefore, the evaluation of the new tram depot locations should represent different aspects, e.g., economical, technical, environmental, and organizational. To handle such a complex decision problem the authors propose a methodology, which is a composition of the optimisation and multiple criteria evaluation techniques. The developed methodology is experimentally applied to the selection of one out of five tram depot locations in the public transport system of the city of Poznan, Poland. All the computational experiments are performed by means of optimization and multiple criteria decision aiding (MCDA) methods and tools, i.e. a linear optimization engine Solver Premium Platform and AHP method with its application AHORNsimple. The calculations are the basis for recommending the location of a new depot in the central part of the transport system network, which is a reasonable solution taking into account, e.g. the proximity of the main railway line, the possibility of triple distribution of the transport means from depot. The proposed methodology of the decision problem solution gives also an opportunity to create the hierarchy of considered tram depot locations as well as to compare the position in the ranking of the best solution with the existing one. Since the proposed methodology assumes the selection of the most suitable MCDA method to the problem under consideration and the decision maker’s preferen¬ces, it guarantees that the result of analysis becomes reliable and the decision aiding process is credible.

User Expectation of Public Transport Design Experience for Electric Bike Sharing in Indonesia

Recently, Indonesia has been rapidly developing public transport infrastructure. However, many people still prefer using a private vehicle for their daily mobility. This study explored the design direction of an electric bike sharing system (EBS) for the first mile/last mile (FMLM) as part of an integrated public transport system to attract more people to public transportation. A focus group interview (FGI) was performed with ten Indonesian students currently studying at Daegu City, South Korea. All of them had experience using public transportation in both Indonesia and South Korea. From the FGI, the data on user needs related to FMLM was analyzed to understand the gap between expectations and reality as well as to enhance the feasibility of the design and development of EBS. In the next stage, based on the various needs and expectations of the participants about ideal public transportation in Indonesia, design strategies and elements were achieved and optimized specifically for EBS. Furthermore, we propose two usability scenarios in order to meet the user requirements. This study will be proposed to traffic policymakers for use in developing a design strategy and direction for an eco-friendly EBS formulation.

Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems

A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.

Data-Driven Performance Evaluation Framework for Multi-Modal Public Transport Systems

Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users’ behaviors must be considered. To this end, a data-driven performance evaluation based on passengers’ actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin–destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months’ worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems.