Candidate Routes Research Articles

Neural Network based Route Guidance Strategy in intelligent transportation systems

Traffic congestion in urban areas presents a significant challenge to efficient transportation and sustainable urban development. This paper introduces a Neural Network based Route Guidance Strategy to address the non-linear complexities of urban traffic flow. The strategy demonstrates its capability to optimize traffic flow in real-time by predicting the travel times of candidate routes. A comprehensive simulation study compares the performance of the Neural Network Strategy with traditional traffic management strategies, focusing on traffic flow efficiency, vehicle count stability, and route choice optimization. The results indicate that the Neural Network Strategy significantly enhances traffic management by stabilizing vehicle counts, reducing fluctuations in traffic flux, and achieving a more uniform distribution of vehicles. The paper concludes with an analysis of the experimental results, highlighting the integration of neural networks into traffic management systems as a promising approach to mitigating urban traffic congestion. Future research directions are discussed, emphasizing the potential for real-world implementation and the exploration of advanced neural network models.

Thermal stability of electrodeposited nanostructured high-entropy alloys

Over the past decade, the study of nanostructured high-entropy alloys (HEAs) has attracted great attention due to their high strength (grain boundary hardening) and improved thermal stability over conventional nanostructured metals and alloys. However, a lingering issue yet to be resolved by these studies is that of commercial viability; the synthesis processes used to date are costly, not easily scalable, and energy intensive. This work examines the thermal stability and mechanical properties of electrodeposited nanostructured HEAs, towards establishing a cost-effective and versatile synthesis route to commercialize this underutilized material class. Nanocrystalline, amorphous, and nanoglass alloys of NiFeCoW, NiFeCoMo, and NiFeCoMoW were systematically characterized to assess the stability of their structure and mechanical properties, compared against conventional nanocrystalline materials and HEA counterparts. The structural stability of these electrodeposited HEAs was shown to improve with increasing number of elements, from a peak temperature for grain growth of ∼270 °C in pure nanocrystalline Ni, to ∼500 °C in electrodeposited HEAs, corresponding to a near doubling of the activation energy for grain growth from 1.3 eV (Ni) to a maximum of 3.1 eV (NiFeCoW). This improved stability was matched with a 60 % increase in hardness after annealing to the point of nanograin nucleation (∼500 °C), to a maximum hardness of ∼8.4 GPa. Such hardening trends followed an extrinsic inverse-to-regular Hall-Petch relationship, where the inverse regime was largely dominated by grain boundary relaxation and nanoglass structural breakdown. With these results, we have established electrodeposition as a promising candidate route for fabricating nanostructured HEAs for applications at elevated temperatures.

An artificial fish swarm optimization algorithm for the urban transit routing problem

The Urban Transit Routing Problem (UTRP) is an NP-hard discrete problem that deals with the design of routes for public transport systems. It is a highly complex, multiply constrained problem, while the evaluation of candidate route sets can prove both challenging and time-consuming, with many potential solutions rejected on the grounds of infeasibility. Due to its difficulty, metaheuristic methods, such us swarm intelligence algorithms, are considered highly suitable for the UTRP. The suitability of these methods heavily relies on the correct adaptation of the chosen method for a discrete-space problem, the initialization procedure, and the solution evaluation method. In this context, this study proposes an artificial fish swarm optimization algorithm for the efficient solution of the UTRP, presenting a novel discrete-space adaptation of the former. The results are subsequently compared to 14 other algorithms, including evolutionary, swarm intelligence and hyper-heuristic implementations, using Mandl’s widely used and accepted benchmark. Comparison of the produced solutions with published results on Mandl’s benchmark network, shows that the developed algorithm yields superior results to the existing techniques, yielding very high shares of direct trip coverage, which is vital for transit systems to attract riders and contribute to urban sustainability. A new indicator for operator cost calculation is also developed and integrated into our analysis, offering insights on the trade-offs between user and operator costs. Differences in generated solutions, influenced by the weighting factor value, can result in variations of up to 13% in direct trip coverage and 1.5 minutes in average travel time.

Emotional experiences and psychological well-being in 51 countries during the COVID-19 pandemic.

The COVID-19 pandemic presents challenges to psychological well-being, but how can we predict when people suffer or cope during sustained stress? Here, we test the prediction that specific types of momentary emotional experiences are differently linked to psychological well-being during the pandemic. Study 1 used survey data collected from 24,221 participants in 51 countries during the COVID-19 outbreak. We show that, across countries, well-being is linked to individuals' recent emotional experiences, including calm, hope, anxiety, loneliness, and sadness. Consistent results are found in two age, sex, and ethnicity-representative samples in the United Kingdom (n = 971) and the United States (n = 961) with preregistered analyses (Study 2). A prospective 30-day daily diary study conducted in the United Kingdom (n = 110) confirms the key role of these five emotions and demonstrates that emotional experiences precede changes in well-being (Study 3). Our findings highlight differential relationships between specific types of momentary emotional experiences and well-being and point to the cultivation of calm and hope as candidate routes for well-being interventions during periods of sustained stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Generating Low Cost and High Throughput Candidate Routes for Evaluation in Route Selection for Hybrid Peptide Synthesis─A Mathematical Optimization Approach

Generating Low Cost and High Throughput Candidate Routes for Evaluation in Route Selection for Hybrid Peptide Synthesis─A Mathematical Optimization Approach

A line planning approach based on time-varying demand for high-speed rail under the combined operation of periodic and aperiodic services

Passenger demand for high-speed rail systems is highly fluctuating in temporal and spatial throughout the day. In order to effectively improve passenger satisfaction and service level for high-speed rail, the formulation of line plan must make the distribution of the trains’ capacities be meet with the time-varying demand of passengers. The aperiodic line plan can well meet this demand since it is more flexible with different stop patterns and various headways. However, the irregular arrival/departure times are not convenient for travel planning. We propose a new type of line plan that would combine both the regularity of the periodic line plan and the flexibility of the aperiodic ones. An optimization method of high-speed railway line plan based on a combination of periodic and aperiodic operation is structured, which can well satisfy the temporal-spatial distributions of passenger demands in different time periods. The main challenge is to coordinate the numbers of periodic and aperiodic trains, and their temporal-spatial distributions on the rail network considering the passengers’ train-choice decisions. Based on generating a set of candidate routes and constructing a passenger travel network, we propose a model to optimize the numbers of both periodic and aperiodic trains, and their paths, stops, approximate arrival and departure times at each visited stations for minimizing the total operation cost of trains and the total generalized travel cost of passengers. Then, a simulated annealing algorithm is designed to efficiently solve our proposed optimization model. Finally, numerical experiments based on the Shanghai-Nanjing intercity railway in China verify the effectiveness and practicability of the optimization method.

An efficient Clustered IoT (CIoT) routing protocol and control overhead minimization in IoT network

Internet of Things (IoT) is a novel networking communication field in which the nodes act as physical objects. These nodes might be stationary or mobile. The majority of networking communication problems occur due to nodes’ mobility. Due to the mobility of IoT networks, excessive control overhead messages are generated. These messages are generated due to the frequent exchange of some control information, such as device identity, location information, device mobility, device direction, and others. In general, network clustering helps to alleviate the problem of generating high control overhead messages. Therefore, many proposed or enhanced Cluster-Based Routing (CBR) protocols address the shortcoming of traditional CBR protocols. In CBR protocols, each cluster selects only one node based on some parameters, and this node is called a LEaDeR (LEDR). The LEDR acts as a coordinator for its associated cluster. Furthermore, the rest of the nodes located in the cluster are called Smart DEVices (SDEV). A problem occurs when the SDEVs and the LEDR exchange their control overhead messages with each other. The exchanging of these messages utilizes the available network resources. This article proposes a Clustered IoT (CIoT) routing protocol, the CIoT routing protocol applies in a grid clustered topology. The proposed CIoT protocol aims to efficiently propagate messages between any pair of devices. The CIoT protocol selects only one route among a set of candidate routes, and the selected route is verified and evaluated based on some constraints. Also, this article proposes a Predictive Generated Hello Messages Algorithm (PGHMA). The PGHMA aims to minimize the generated control overhead messages produced by SDEVs and LEDRs. In comparison to other CBR protocols, simulation results demonstrate the effectiveness of the CIoT algorithms by increasing network throughput by 78% and decreasing end-to-end latency by 12.5%. Also, the PGHMA algorithm significantly outperforms other algorithms in terms of reducing the number of generated HELLO messages by 5.9%. Finally, we implement and evaluate our proposed algorithms by comparing them to other related algorithms published in the literature.

A matheuristic algorithm for the share-a-ride problem

This research studies the Share-a-Ride Problem (SARP) in which a set of taxis is used to serve a set of package and passenger requests at the same time. The goal is to maximize the profit from serving the requests without violating given constraints. We develop a new matheuristic algorithm that combines the simulated annealing with mutation strategy (SAMS) and the set partitioning (SP) approach to improve the reported solutions of SARP benchmark instances. The SAMS consists of a time-slack strategy, neighborhood moves, a mutation strategy that depends on the time-slack strategy, and a penalty mechanism for infeasible solutions. The first phase of the proposed matheuristic uses SAMS to generate a set of feasible candidate routes. A route accumulation mechanism is added to the SAMS to keep the candidate routes in the route pool. The second phase of the matheuristic focuses on solving the set partitioning model to find the best route combination as the final solution. The proposed matheuristic is tested on existing small and large SARP instances, and a set of newly generated large SARP instances and then compared with SAMS algorithm. The experimental results show that the proposed matheuristic obtains optimal solutions to all the small SARP benchmark instances. For large instances, the proposed matheuristic outperforms SAMS algorithm as it obtains several new best solutions to SARP. Lastly, our algorithm obtains high-quality solutions to the new large SARP instances generated in this study.

Routing, Modulation Level, and Spectrum Assignment in Elastic Optical Networks—A Serial Stage Approach with Multiple Sub-Sets of Requests Based on Integer Linear Programming

This paper addresses serialized approaches of the routing, modulation level, and spectrum assignment (RMLSA) problem in elastic optical networks, using multiple sequential sub-sets of requests, under Integer Linear Programming (ILP). The literature has reported two-stage serial optimization methods referred to as RML+SA, which retain computational efficiency when the problem grows, compared to the classical one-stage RMLSA optimization approach. However, there still remain numerous issues in terms of the spectrum used that can be improved when compared to the RMLSA solution. Consequently, this paper proposes RML+SA solutions considering multiple sequential sub-sets of requests, split traffic flow, as well as path-oriented and link-oriented routing models. Simulation results on different test scenarios determine that: (a) the multiple sequential sub-sets of request-based models improve computation time without worsening the spectrum usage when compared to just one set of requests optimization approaches, (b) divisible traffic flow approaches show promise in cases where the number of request sub-sets is low compared to the non-divisible counterpart, and (c) path-oriented routing succeeds in improving the used spectrum by increasing the number of candidate routes compared to link-oriented routing.

Development of a Logistics System for Disaster Medical Container Operations Using Drones and GIS

Introduction:To quickly operate a temporary hospital using disaster medical containers in the event of a large-scale disaster, dozens of large trailers must be quickly brought to the disaster site. This study aims to establish a logistic system for transporting and installing containers using drones and GIS.Method:By linking drones and GIS, a theoretical model was created to quickly determine routes and installation sites for large trailers transporting medical containers from candidate routes simulated in advance by taking into account hazard maps and road conditions.Results:Using this system, it is possible to(1) Establish whether the roads planned to be passed through are passable.By narrowing down candidate routes through preliminary simulations that overlay hazard maps and roads that are passable for large trailers, it is possible to narrow down the routes that need to be confirmed. Immediately after a disaster, a drone can be used to confirm road damage, flooding, landslides, etc., and map them on the GIS to determine the extent to which they are passable.(2) Identify locations that take into account demand and safety.Locations that are close to the disaster area, safe, and in demand for medical care can be determined and identified based on the population mesh and the damage situation.Conclusion:GIS and drones have a high affinity and are used in various ways. However, there is still no linkage between GIS and drones adapted to medical care and logistics at disaster sites in Japan. This system is indispensable to comprehensively determine the safety and demand of roads and installation sites.

De Novo Synthesis of Poly(3-hydroxybutyrate-co-3-hydroxypropionate) from Oil by Engineered Cupriavidus necator.

Poly(3-hydroxybutyrate-co-3-hydroxypropionate) [P(3HB-co-3HP)] is a biodegradable and biocompatible polyester with improved and expanded material properties compared with poly(3-hydroxybutyrate) (PHB). This study engineered a robust malonyl-CoA pathway in Cupriavidus necator for the efficient supply of a 3HP monomer, and could achieve the production of [P(3HB-co-3HP)] from variable oil substrates. Flask level experiments followed by product purification and characterization found the optimal fermentation condition (soybean oil as carbon source, 0.5 g/L arabinose as induction level) in general consideration of the PHA content, PHA titer and 3HP molar fraction. A 5 L fed-batch fermentation (72 h) further increased the dry cell weight (DCW) to 6.08 g/L, the titer of [P(3HB-co-3HP)] to 3.11 g/L and the 3HP molar fraction to 32.25%. Further improving the 3HP molar fraction by increasing arabinose induction failed as the engineered malonyl-CoA pathway was not properly expressed under the high-level induction condition. With several promising advantages (broader range of economic oil substrates, no need for expensive supplementations such as alanine and VB12), this study indicated a candidate route for the industrial level production of [P(3HB-co-3HP)]. For future prospects, further studies are needed to further improve the strain and the fermentation process and expand the range of relative products.

Resilience-based optimization model for emergency bus bridging and dispatching in response to metro operational disruptions.

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.

Scatter-GNN: A Scatter Graph Neural Network for Prediction of High-Speed Railway Station—A Case Study of Yinchuan–Chongqing HSR

The Yinchuan–Chongqing high-speed railway (HSR) is one of the “ten vertical and ten horizontal” comprehensive transportation channels in the National 13th Five-Year Plan for Mid- and Long-Term Railway Network. However, the choice of node stations on this line is controversial. In this paper, the problem of high-speed railway station selection is transformed into a classification problem under the edge graph structure in complex networks, and a Scatter-GNN model is proposed to predict stations. The article first uses the Node2vec algorithm to perform a biased random walk on the railway network to generate the vector representation of each station. Secondly, an adaptive method is proposed, which derives the critical value of edge stations through the pinching rule, and then effectively identifies the edge stations in the high-speed railway network. Next, the calculation method of Hadamard product is used to represent the potential neighbors of edge sites, and then the attention mechanism is used to predict the link between all potential neighbors and their corresponding edge sites. After the link prediction, the final high-speed railway network is obtained, and it is input into the GNN classifier together with the line label to complete the station prediction. Experiments show that: Baoji and Hanzhong are more likely to become node stations in this north–south railway trunk line. The Scatter-GNN classifier optimizes the site selection strategy by calculating the connection probabilities between two or more candidate routes and comparing their results. This may reduce manual selection costs and ease geographic evaluation burdens. The model proposed in this paper can be used as an auxiliary strategy for the traditional route planning scheme, which may become a new way of thinking to study such problems in the future.

Spectrum allocation using multiparameter optimization in elastic optical networks

The Min Slot-Continuity Capacity Loss (MSCL) algorithm is listed in the literature as a powerful algorithm to solve the spectrum assignment (SA) problem in elastic optical networks (EONs). The MSCL presents, in its structure, characteristics that allow it to consider several unique network parameters that impact the network performance during the decision of the spectral allocation, such as: the evaluation of the impact of a spectral allocation in both the candidate route and in its interfering routes and the evaluation of the impact of a spectral allocation on the network’s capacity to successfully allocate future demands. However, other interesting features can be still added to the MSCL. Thus, in this paper we propose a framework that enables a systematic design of heuristics SA algorithms based on the MSCL principles. The main building blocks of the framework are the MSCL principles, the inclusion of new metrics and how to associate them, a generic function expanded in a series of functions and the utilization of a methaheuristic optimization algorithm. We apply the proposed framework to design two new SA heuristics named as MPAO-Wj and MPAO-WLj. These heuristics use the particle swarm optimization as optimization engine. We carried out computational simulations to assess the performance of the proposed algorithms and we compared them against five other benchmark heuristics from the literature. The simulation results indicate that, in the considered scenarios, the proposed algorithms are able to achieve superior network performance (in terms of blocking probability) than the investigated benchmark algorithms.

Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers’ Route Choices

This paper explores the relationships between travel time index and planning time index, two proven indices used to measure real-time congestion and travel time reliability, and their impacts on truck drivers’ route choices through training of a predictive model based on a machine learning algorithm—eXtreme Gradient Boost (XGBoost). Moreover, this study adopts an interpretable machine learning framework called SHapley Additive ExPlanation (SHAP) in the predictive model to reveal the insights usually hidden inside the machine learning “black box.” The predictive model is trained through a truck trajectory dataset provided by the Maryland Department of Transportation State Highway Administration and INRIX. The classical logistic regression model is adopted as the baseline model. The results show the XGBoost model can better handle nonlinearity and provide more reliable predictions. Through the SHAP framework, the results indicate that mobility and reliability indices and total trip time nonlinearly influence route choices. Truck drivers are more sensitive to real-time congestion information and reliability information when the differences in mobility and reliability indices on candidate routes reach certain thresholds. Moreover, the interaction study on trip time and mobility index shows that truck drivers are more sensitive to real-time congestion information if the candidate routes’ travel time is a larger portion of the total trip time.

CRCLoc: A Crowdsourcing-Based Radio Map Construction Method for WiFi Fingerprinting Localization

The WiFi-based fingerprint indoor-positioning system has attracted increasing interest from industry and academia, benefiting from the widespread deployment of the wireless local area network (WLAN) infrastructure. However, with the expansion of application scenarios, this system suffers from labor-intensive work for received signal strength (RSS) fingerprint construction. In this article, we propose a crowdsourcing-based radio map construction method and trajectory matching algorithm to solve the time-consuming preliminary fingerprint data collection in the offline phase, where the tedious collection work is replaced by the massive crowdsourcing sensor data. First, the latent information of the map is extracted by using some image processing methods, and all possible routes are obtained simultaneously with the depth-first traversal method. Then, considering the strict restrictions on walking in indoor environments, the crowdsourcing trajectories are produced by matching the result of pedestrian dead reckoning (PDR) with the candidate routes based on the Shape Context algorithm. Further, a crowdsourcing trajectory can be represented by uniformly distributed reference points based on step detection, and each point corresponds to a unique RSS of access points (APs). Since such a fingerprint construction method can be performed while smartphone holders are moving and not aware of their actual positions, it can be referred to the dynamic construction method of the radio map. The real scenario experiments reveal that the proposed solution can significantly reduce the time and manpower consumption to build the radio map. Moreover, compared with traditional schemes, our crowdsourcing-based radio map construction method for the WiFi fingerprinting localization (CRCLoc) system can improve the accuracy and robustness of the indoor-positioning results.

A MIP-based heuristic for a single trade routing and scheduling problem in roll-on roll-off shipping

We study a single trade ship routing and scheduling problem for a roll-on roll-off shipping company. Along the given trade, there is a number of contracts for transportation of cargoes between port pairs. Each contract states a minimum service frequency where the services should be evenly separated in time and possibly transit time requirements. Current planning practice is to visit all ports along the trade every time it is serviced. Here, we aim instead at determining the sailing route and schedule of each voyage along the trade, i.e., which ports to visit when, which contracts to serve, and the sailing speeds, so that all contract requirements are satisfied at minimum cost. To solve this problem, we have developed a three-phase MIP-based heuristic, where each phase consists of solving dedicated a mixed-integer programming (MIP) model. The heuristic constructs solutions by first identifying the most promising candidate routes along the trade. Next, a candidate route is allocated to each available vessel. Finally, the heuristic determines the allocation of cargoes between the vessels, as well as sailing speeds and arrival times. Computational tests show that the heuristic outperforms a commercial MIP-solver and provides high-quality solutions to realistically sized instances in reasonable time.

Toward Bio‐Sourced Elastomers with Reactive/Polar Groups. Myrcene – Glycidyl Methacrylate Copolymerization: Reactivity Ratios, Properties, and Preliminary RAFT Emulsion Polymerization

AbstractThere is a pronounced need of replacing fossil‐origin materials with sustainable ones. Myrcene (Myr) is a biobased terpene‐type monomer derived from diverse plants that is a potential substitute for diene monomers (e.g., butadiene), in the manufacture of elastomers. Here, the copolymerization of Myr with glycidyl methacrylate (GMA) is studied as a candidate route for producing functional/polar elastomers. Reactivity ratios (RR) for this system are estimated using the error‐in‐variables method resulting in rMyr = 0.519 ± 0.062 and rGMA = 0.301 ± 0.014, indicating an alternating tendency. The copolymers obtained exhibit number‐average molecular weights (Mn) in the range 42–674 kDa that markedly increase with the GMA content and dispersities in the range 1.49–1.90. The addition of Myr units predominantly results (1H‐NMR) in the 1,4 cis microstructure (94–97%) that increases with the GMA content. Differential scanning calorimetry analysis is performed in some of the copolymer samples. Additionally, preliminary emulsion copolymerizations of Myr‐GMA are carried out at 70 °C, 15% solids, with potassium persulfate as initiator and 4‐cyano‐4‐[(dodecyl sulfanylthiocarbonyl)sulfanyl] pentanoic acid as RAFT agent, yielding conversions of 74–85% in 24 h and gel contents of 17–78% that increase with increasing GMA (0–21%) fed. The GMA presence in the copolymer is demonstrated by fourier transform infrared spectroscopy (FTIR) analysis.

A novel Sequence-Aware personalized recommendation system based on multidimensional information

Due to the rapid growth of the information overload issue, recommender systems have become necessary and are implemented in numerous facets of human life, including the tourism industry. Today, technological advancements have significantly altered our travels, and these advancements promise even more interactive and exciting experiences in the future. Nowadays, planning and arranging a customized trip well in advance is ideal, as the process can be challenging and time-consuming. This paper proposes a hybrid approach that leverages multidimensional data to enhance personalized trip recommendations while addressing several shortcomings of existing recommender systems, such as their inability to account for dynamic user preferences and diverse contexts. To this end, the proposed method, considering the data sparsity issue, employs a clustering algorithm to reduce the time complexity of discovering Points of Interest (POIs). This approach utilizes user demographic information to address the cold start issue while improving the collaborative filtering (CF) paradigm through an asymmetric schema. Furthermore, this study exploits the context vector model and the Term-Frequency-Inverse-Document-Frequency (TF-IDF) algorithm to present a novel method for quantifying context similarity. Moreover, the method retrieves and ranks a list of candidate routes optimized by applying personalized POIs to sequential travel patterns. Finally, the experimental results demonstrate the superiority of the approach in terms of Precision, Recall, RMSE, MAP, F-Score, and nDCG compared to previous works based on Flickr and STS datasets.

Prioritizing Bus Routes for Electrification: GIS-Based Multi-Criteria Analysis Considering Operational, Environmental, and Social Benefits and Costs

Society today is enjoying an unprecedented level of human mobility but is also confronting environmental degradation resulting from fossil fuel consumption and greenhouse gas emissions. The electrification of bus transit systems is recognized as one of the practical solutions to mitigate air pollution and other externalities of increased mobility. However, the implementation of an e-bus system requires the purchase of e-buses and the development of charging infrastructure. To reduce costs and maximize benefits, it is crucial to develop an integrated strategy during the planning stage. This study applies a GIS-based multi-criteria decision analysis approach to determine the candidate bus routes to convert from diesel-powered to electric-driven. This framework appraises not only the characteristics of bus routes but also the possibility of deploying charging infrastructures in bus terminals. Fourteen common criteria are used to evaluate the main considerations of bus electrification, including economic, environmental, and social benefits and costs. The analytic hierarchy process and the technique of order preference similarity to the ideal solution are employed to determine the criteria weights and the route ranking, respectively. The bus network of Twin Cities, MN, U.S., is used as a study case to present the proposed approach. Sensitivity analysis is included to identify the overall top 10 bus routes. The result shows that this method can use widely available open data to select top candidate routes that meet multiple criteria.