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In this paper, a dynamic entry pedestrian flow assignment problem is investigated. The problem concerns a scenario, i.e. a number of pedestrians enter a public place with multiple entrances and internal obstacles, and then get to their destinations in the public place. The problem aims at determining the access entrance, moving route, and destination position choice of pedestrians so as to minimize the total entry time of all pedestrians. A mesoscopic model is developed to formulate the dynamic entry process of pedestrians in the scenario and a heuristic algorithm is proposed to solve the dynamic entry pedestrian flow assignment problem. Moreover, a set of numerical investigations are provided to demonstrate the applications of the model and the effectiveness of the algorithm, especially to a case with a larger number of entry pedestrians. [ABSTRACT FROM AUTHOR]

In this age of cutting-edge technology, electronic equipment consumption, such as mobile phones, has increased tremendously. This was followed by a staggering increase in electronic waste (e-waste). However, it has been known that e-wastes contain higher amounts of precious metals than pristine sources, such as Au, which is valuable as an alternative precious metal source. Due to its various advantages, adsorption has emerged as one of the most common metal removal or recovery methods. A crucial aspect of adsorption is developing a cost-effective adsorbent for selective recovery of Au from the e-wastes. Thus, functionalized biomass-based adsorbent was developed to recover precious metals from e-waste. Functionalized activated carbon (ACIL900) has an appealing high adsorption capacity (116.2 mg g−1) performance and follows the chemisorption route, which fits with the monolayer model. The presence of IL boosts the adsorption efficiency due to its compatible interaction with the metal complexes. Remarkably, ACIL900 could quantitatively adsorb precious metals and several base metals from mobile phone leachate. The selective recovery of Au from the adsorbent was performed by sequential desorption using various solutions. Finally, Au was successfully recovered from the desorption solution using an electrochemical reduction process. ACIL900 was also found to maintain its performance for three adsorption–desorption cycles. This work reported the full recovery of Au from e-waste to its final pure product through the combination of adsorption, sequential desorption, and electrochemical reduction. [ABSTRACT FROM AUTHOR]

Under the government-to-government cooperation framework, the Memorandum of Understanding (MoU) on China–Singapore International Land–Sea Trade Corridor (C–S-ILSTC) between Singapore and Chongqing has been signed in 2018, with the objective to improve the connectivity between Southeast Asia and Western China region via the Beibu Gulf port. C–S-ILSTC provides a new and efficient intermodal freight transportation route choice, which is expected to reconstruct the regional intermodal container transportation network. In this study, we formulate a discrete route choice model based on stated preference survey to analyse the route choice behaviour between Southeast Asia and western China region under the intermodal container transport network with C–S-ILSTC. Based on the modelling results, we further discuss the impact of significant route choice influential factors, such as transit time, shipping fee, cargo type, historical experience of using the route, company size, annual throughput, and business area. In addition, descriptive statistics and word cloud analysis are provided to investigate respondents' underlying attitudes on the most important route choice influential factors. Scenario analysis is further conducted to demonstrate the impact of subsidy on respondents' willingness to switch to the newly introduced C–S-ILSTC for freight transportation. The results provide policy implications on improving the utilisation rate of C–S-ILSTC. • Analyse the route choice behaviour regarding C–S-ILSTC. • Formulate a discrete route choice model based on stated preference survey. • Investigate the impact of significant route choice influential factors. • Scenario analysis on the impact of subsidy. • Provide policy implications on improving the utilisation rate of C–S-ILSTC. [ABSTRACT FROM AUTHOR]

This paper evaluates how the route overlapping feature affects the stochastic assignment paradox by comparing the multinomial logit (MNL) model and two path-size logit (PSL) models. Two circumstances, (1) improving an existing link and (2) adding/removing a new link, are studied. The paradoxical features of three models are illustrated and compared mathematically and graphically in four networks with route overlapping links: (1) a single origin-destination (O-D) pair network with four links; (2) a single O-D pair network with five links; (3) a multiple O-D pair network with seven links; and (4) an application in a road sub-network of Harbin City. The general conclusions are significant for traffic planners and engineers when those kinds of route choice models are used to evaluate traffic planning, management, and control policies/measures. [ABSTRACT FROM AUTHOR]

In this study, an effective travel cost (ETC) traffic equilibrium model is proposed for multi-modal networks with uncertain demands. The multi-modal networks are transformed into supernetworks and travel demands are assumed to be closed intervals. Passenger flows and travel costs are also formulated as closed intervals to capture the effects of uncertain demands. The ETC concept is introduced and regarded as a choice criterion to develop an equilibrium model which captures travellers' travel mode and route choice behavior under interval travel costs. The model is formulated as a variational inequality problem, and the method of successive average algorithm is adapted to interval mathematics to obtain the results in the form of interval variables. Illustrative examples are also presented to demonstrate the model's characteristics and its differences from the traditional equilibrium model, in which the expected travel cost is regarded as the choice criterion. [ABSTRACT FROM AUTHOR]

Infrastructure has contributed to eradicating poverty and improving the livelihood of people. In Sub-Saharan Africa, active transport infrastructure is less, poor, or lacking, which has become one source of the decline in cycling and efficiency in walking. This study sought to investigate the types of infrastructure or routes that active transport users are exposed to, determine their usage magnitude and solicit views regarding them through questionnaire surveys and screen-line counts in two major cities in Ghana. Active transport respondents who comprised of pedestrians and a few cyclists identified themselves with these infrastructures and route use frequently: streets, sidewalks, cycle lanes, unpaved footpaths, and 'lungulungus'. Street and 'lungulungus' were the most used infrastructure or route choices by all age, gender, and occupation groups in both study locations at Ayawaso East Municipal Assembly and Tamale Metropolitan Assembly. The respondents chose to use these two mainly because they were direct, cohesive and safe. The fact that people used 'lungulungus' more than the provided infrastructures suggests poor planning of the built environment and inadequate dedicated infrastructure. This calls for a review of existing dedicated infrastructure and the provision of infrastructure that addresses the needs of pedestrians and cyclists. [ABSTRACT FROM AUTHOR]

While much has been reported about bus travel times and their variability for formal bus services, little is known about travel time variation for paratransit, the dominant means of transportation in most low- and middle-income countries (LMICs). This study quantifies the components of paratransit travel time on a selected route in Kumasi, Ghana. It analyzes the variability of travel times within the day and from day to day. A mobile phone app was employed to conduct a travel time survey onboard paratransit vehicles on the study route. GPS and stop-related data were collected. Various travel time variability measures and heat map was used for within day and day-to-day variability analysis in both directions of the study section. About 16% of travel time in the study section was spent dwelling (boarding and alighting). The variation in travel times across the day was comparatively higher than those of formal bus services and fluctuated across the day with no distinct pattern within any given time period. Both early and late trips contributed to this variation across the day. Fridays had significantly different variability from other weekdays. [ABSTRACT FROM AUTHOR]

From the view of comprehensive consideration, this considers the uncertainty of transportation time and cost due to seasonality and human factors, and a multi-objective chance constrained programming model with minimum transportation time and cost was established. Due to the complexity of the problem, traditional algorithms are not enough, and more efficient optimization algorithms are still needed. According to the characteristics of the problem, the beetle antennae search (BAS) with one beetle is changed into multiple beetles, and Dijkstra algorithm is embedded, a hybrid beetle swarm optimization algorithm (HBSO) is designed to solve the problem, and the case analysis and algorithm analysis are made for three different examples; the datasets are referenced in literature (Lu et al. in Computer Integrated Manufacturing Systems 5:10–14, 2020). Under the three examples, the influence of the customer on the time and cost is different. The confidence level and weight of cost and time have great influence on the decision results. 4PL provider will not affect the choice of transportation route, but will change the choice of 3PL supplier and transportation mode. HBSO is an effective method to solve this problem. It is proved on efficiency, effectiveness and reliability in experiments by comparing with GA, PSO and BAS. [ABSTRACT FROM AUTHOR]

Biofilms are complex microbial communities formed by the attachment of bacteria or fungi to surfaces encased in a self-produced polymeric matrix. These biofilms are highly resistant to conventional antimicrobial therapies. The resistance mechanisms exhibited by biofilms include low antibiotic absorption, sluggish replication, adaptive stress response, and the formation of dormant-like phenotypes. The eradication of biofilms requires alternative strategies and approaches. Nanotechnological drug delivery systems allow excellent control over the drug chemistry, surface area, particle size, particle shape, and composition of nanostructures. Nanoformulations can enhance the efficacy of antimicrobial agents by improving their bioavailability, stability, and targeted delivery to the site of infection that helps biofilm eradication more effectively. In addition to nanoformulations, the route of administration and choice of dosage forms play a crucial role in treating biofilm infections. Systemic administration of antibiotics is effective in controlling systemic infection and sepsis associated with biofilms. Alternative routes of administration, such as inhalation, vaginal, ocular, or dermal, have been explored to target biofilm infections in specific organs. This review primarily examines the utilisation of nanoformulations in various administration routes for biofilm management. It also provides an overview of biofilms, current approaches, and the drawbacks associated with conventional methods. [ABSTRACT FROM AUTHOR]

• Perception of streetscape design affects commuting route choice of cyclists. • VR and eye-tracking to collect top-down perception data of cyclists. • Map survey to collect routes and streetscape elements of cyclists. • Added value of the method in terms of learning and usability. To encourage behavioural changes that reduce car use, understanding travel behaviour and interventions in infrastructure are necessary. Understanding the route choices of cyclists and their motivation based on their preferences regarding their trip attributes is essential to improve cycling infrastructure and encourage active travel modes such as cycling. This research aims to explore the influence of streetscape design features (SDFs) on cyclists' route choices by developing a mixed-method approach to collect objective and subjective data from 22 volunteers. We used a survey (Maptionnaire) with a virtual reality (VR) and eye-tracking experiment to simulate a bicycle trip in Enschede, The Netherlands. Results showed that despite differences between usual and ideal routes regarding SDFs (such as the presence of vegetation, water bodies, and road intersections), factors such as infrastructure provision and quality have a more significant impact when choosing a route. The novelty of the proposed methodology lies in combining data from the different methods to improve understanding regarding the influence of SDFs in cyclists' route choices and raise awareness among the participants about the influence of SDFs on their route choices. These results may encourage future studies to develop and implement alternative mixed-methods approaches to increase understanding of cyclists' travel behaviour. From a practitioner's perspective, implementing such mixed-method for data collection may increase the efficiency of the process to aid in the development of contextual data-driven interventions that encourage the use of bicycles. [ABSTRACT FROM AUTHOR]

This paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while ensuring a probabilistic service requirement on the waiting time to charge. Electric vehicle users or followers, on the other hand, minimize route length and may be cooperative or noncooperative. Their choice of route in turn determines the charging demand and waiting times at the charging stations and hence, the need to account for their decisions by the leader. The bilevel problem reduces to a single-level mixed-integer model using the optimality conditions of the follower's problem when the charging stations operate as M/M/c queues and the followers are cooperative. To solve the bilevel model, a decomposition-based solution methodology is developed that uses a new logic-based Benders algorithm for the location-only problem. Computational experiments are performed on benchmark and real-life highway networks, including a new eastern U.S. network. The impact of route choice response, service requirements, and deviation tolerance on the location and sizing decisions are analyzed. The analysis demonstrates that stringent service requirements increase the capacity levels at open charging stations rather than their number and that solutions allowing higher deviations are less costly. Moreover, the difference between solutions under cooperative and uncooperative route choices is more significant when the deviation tolerance is lower. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Funding: This research was supported by the Ontario Graduate Scholarship when Ö. B. Kınay was a PhD candidate at the University of Waterloo, and this support is acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0494. [ABSTRACT FROM AUTHOR]

The built environment, cyclist behavior, and driver interactions can influence route choice and, ultimately, cyclist safety. Recent studies use crowdsourced data, such as Strava, to document route selection; however, aggregated data may not fully explain the factors underpinning route selection. Utilizing naturalistic methods, we analyze videos of recorded rides and GPS data from six cyclists representing three types of riders—commuters, recreational, and athletes—to explore route choices, behavior, and driver interactions in Tucson, Arizona. Our analysis of three route selection cases highlights how intersection design, driver interactions, pavement conditions, and type of riding lead cyclists to modify behaviors and select longer detours to avoid unsafe intersections. Additionally, our study combines Strava heatmaps and physical bicycle counts to explore the number of cyclists potentially facing similar factors influencing route choice. By studying cyclists with different riding aims and utilizing both Strava heatmaps and video recordings, researchers can determine the underlying conditions, identify route locations in need of improvements, and collaborate with practitioners to implement changes to increase cyclist safety through appropriate solutions. This analysis can help ensure that designs meet user expectations. [ABSTRACT FROM AUTHOR]

The advanced travel information system is a prominent intelligent transportation system for which variable message signs (VMSs) are the major components. In Iran, more attention has been paid to sign placement than to other components which are complementary to placement and combine to create relevant and effective messages for the driver. The current study used a stated preference survey to investigate the attitudes and reactions of drivers to VMS information that then were used to build models to reveal the reasons behind driver reactions to a VMS request to change route. The survey included variables affecting driver decisions, such as driver characteristics, attitude and perceptions of current VMS information and traffic in general. The influence of personality traits as subjective factors for driver behavior regarding route choice also was investigated. The results of the estimation of sequential hybrid binary logistic regression model showed that visual health had a significant positive effect on the driver decisions to divert to an alternate route when the VMS messages related to an accident or traffic congestion. Provision of expected delay information about the current route was found to be the most significant factor causing drivers to change route when a VMS indicated traffic congestion ahead. Personality traits such as Agreeableness, neuroticism, extraversion and openness significantly affected driver decisions about changing route when the VMS indicated traffic congestion or adverse weather conditions. [ABSTRACT FROM AUTHOR]

A key input to quantitative evaluations of transport infrastructure projects is their impact on transport costs. We propose a new method of estimating this impact relying on widely accessible customs data: by using the route choice of exporters. We combine our method with a spatial equilibrium model to study the effects of the massive expressway construction in China between 1999 and 2010. We find transport costs are 20% lower on expressways than on regular roads. The expressways construction increases aggregate exports by 10% and domestic trade by 14%. It generates 5.1% welfare gains, implying a 150% net return to investment. [ABSTRACT FROM AUTHOR]

Many studies have shown that air quality in cities is affected due to emissions of carbon from vehicles. As a result, policymakers (e.g., municipalities) intensely search for new ways to reduce air pollution due to its relation to health diseases. With this concern, connected vehicle technologies can leverage alternative on-road emissions control policies. The present investigation studies the impact on air pollution by (i) updating vehicles' routes to avoid pollution exposure (route choice policy), (ii) updating vehicles' speed limits (speed control policy), and (iii) considering electric vehicles (EVs). Vehicles are informed in advance about route conditions (i.e., on-road emissions) using the vehicular network. We found that by updating vehicle routes, 7.43% less CO emissions are produced within the evaluated region. Also, we find no evidence of significant emissions reductions in the case of limiting vehicles' speed. Lastly, with 30% of EV penetration, safe CO emissions levels are reached. [ABSTRACT FROM AUTHOR]

In the realm of transportation system optimization, enhancing overall performance through the proactive coordination of traffic guidance and signal control in a divergent network can tackle the challenges posed by traffic congestion and inefficiency. Thus, we propose an innovative approach to first allow the information on variable message signs (VMS) that deviates from estimated travel times. This proactive approach guides drivers towards optimal routes from a system-wide perspective, such as minimizing vehicle hours traveled. The deviation is constrained both by the lower bound of drivers' long-term compliance rate and the upper bound of the favored traffic signal operation. The proposed approach coordinates the traffic guidance system with the signal control system. The traffic signal control system sets the upper limit for information deviation in the traffic guidance system, while the traffic guidance system provides demand predictions for the traffic signal control system. Overall, the objective function of the approach is the network-level performance of all users. We gauge traveler satisfaction as a measure of system credibility, using both a route choice module and a satisfaction degree module established through stated preference surveys. Numerical results demonstrate that proactive-coordinated (PC) strategies outperform reactive-coordinated (RC), proactive-independent (PI), and reactive-independent (RI) strategies by improving the system performance, meanwhile keeping the system trustworthy. Under the normal traffic scenario, the PC strategy reduces total travel time by approximately 10%. Driver satisfaction with the PC strategy increases from a baseline of 76% to 95%. Moreover, in scenarios with sudden changes in either traffic demand or supply, e.g., accidents or large events, the proactive guidance strategy is more flexible and can potentially improve more from the system perspective. [ABSTRACT FROM AUTHOR]

Clock tree synthesis (CTS) is an important process in determining overall chip timing and power consumption. The CTS is also a time-consuming process for checking the clock tree. If the chip design and specification do not match, the CTS result will be wrong. Many users use licensed electronic design automation (EDA) tools like Synopsys, and Cadence to carry out accurate chip verification. However, when using a licensed EDA tool, it is difficult to change the function and confirm the overall process in detail. If the design is wrong, the expected cost is doubled, as it will be necessary to modify the design and check all processes for verification. Currently, it cannot check the synthesizability of the clock tree on the placement and route process using only RTL. The main purpose of this study is to predict the CTS result of pre-estimation roughly using an RTL source placing temporary logics using random buffer insertion before the route process: then the incorrectly designed part can be freely modified because the CTS result can be known in advance. Experimental results showed that this research achieves an increase in inserted buffer area by about 10%, the standard deviation of clock skew achieves zero clock skew after shallow CTS, and clock frequency increases by about 10%. This paper contributes to optimizing clock tree implementation by conducting the pre-route process before using the CTS tool. Also, our approach not only minimizes resource usage but also optimizes CTS for the RTL structure. It holds considerable value in enhancing the efficiency and performance of integrated circuits. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time multi-agent fleet management strategy for autonomous underground mines vehicles. The fleet management strategy is based on multi-agent technology and includes a novel variation of the Contract-Net protocol. This paper also proposes a set of conflict management procedures to deal with the narrow nature of underground drifts, as well as the sequencing of trucks' loading activities. This strategy is tested in a simulated environment based on an industrial case study. Both the strategy and the test environment were implanted using AnyLogic. More specifically, the fleet management activities addressed are dispatching, routing and traffic management of mining vehicles, which deal respectively with the assignment of the next destination to a vehicle that has just completed a task; the choice of the route to be followed to reach the selected destination; and traffic coordination in the underground transportation network, made up of one-lane bi-directional road segments. To evaluate the proposed solution, an agent-based simulation model of a Canadian underground gold mine is implemented with AnyLogic. Results show that the proposed coordination strategy outperform the one currently employed strategy by the mine under investigation. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive framework for estimating passengers' transfer times and extracting their distribution and related transfer routes using WIFI probe data. The departure time of preceding station, arrival time of subsequent station, and train running time are selected to obtain transfer times. Then, the collected data is analyzed using kernel density estimation to obtain candidate distribution. Gaussian mixture models are adopted to extract the distribution of each possible transfer route at both peak hours and off-peak hours. This method is tested at two transfer stations of Xi'an metro system with the comparison of results from automatic fare collection data and manual sampling survey data. The results indicate that the proposed approach can collect the transfer time with a sampling ratio greater than 30% and a deviation less than 5%. The route choice behaviors and distribution of transfer time under various conditions can be identified using the proposed methods. [ABSTRACT FROM AUTHOR]

Determination of transport demand is one of the key factors to solve many transportation problems. Existing methods for obtaining information about passenger mobility have significant shortcomings; currently, methods based on the collection, integration and analysis of big data (Urban computing, big data) are being increasingly used. Within the framework of this approach, a methodology has been developed for determining the passenger traffic by public transport from the operations of validating electronic travel tickets: smart cards, transport cards, magnetic cards, mobile phones or other electronic devices (Electronic Gadgets), their details are recorded in the Automated Fare Collection (AFC). In this work we have taken into account that the passenger can travel one, two or more segments before paying for the trip. On some routes, payment is made at the end of the trip. The article presents a methodology based on defining and evaluating the set of acceptable variants of the connectedness of the passenger trips' sequence, which takes into account many factors that influence the choice of travel routes by the passenger. For example, unlike existing solutions, the possibility of paying for travel at any point of the route is taken into account, not necessarily immediately after boarding the vehicle. Approbation of the considered methodology was carried out on the data of the Krasnoyarsk public transport system for April 2019. It has been proved that passenger traffic from the validation of electronic travel tickets allows us to estimate the parameters of public transport demand within the limits of acceptable statistical errors. [ABSTRACT FROM AUTHOR]

The article presents a new method of predicting the time of completion of a transport task. The method is based on the created mathematical model of the transport task process and on artificial intelligence methods. The choice of the optimal transportation route is based on the conditions of a specific task. A new method of choosing the optimal route is presented, taking into account the influence of such factors as weather conditions, day of the week, time of day, condition of the road surface, presence of residential complexes and other factors. The purpose of the study is to optimize the method of building a transport route using neural networks. The work presents the improvement of the general method of optimizing the transport process, the development of a neural network training method for predicting the time of the transport process, the development of a simulation mathematical model of the transport process and the determination of the model parameters and the effectiveness of the method verification. Research methods: methods of mathematical modeling, simulation modeling, Monte Carlo method, methods of artificial intelligence based on neural networks. [ABSTRACT FROM AUTHOR]

The subject of this study is steganographic systems that are able to hide not only the content of the transmitted information but also the fact of its transmission. In this paper, we present a novel data transmission model within such systems, combining a modified Dijkstra's algorithm with the dynamic utilization of multiple transmission routes. Consequently, this allows the simultaneous transmission of messages through parallel groups of routes with the dynamic alternation of these groups during the information exchange process between the sender and the receiver in the steganographic network. Thus, the speed, reliability, and security of message transmission are improved. Moreover, to increase the security level, the proposed model uses a route–time matrix that is available only to the sender and receiver. Additionally, the data transmission model has been tested on synthetic data and data obtained from real devices. The evaluation of the effectiveness of the proposed model on the example of various structured steganographic systems shows the significant effectiveness of our model. As a result, in comparison with existing similar methods, the secrecy index was increased five times and the violation index was decreased four times. [ABSTRACT FROM AUTHOR]

In order to address the issue of environmental perception in autonomous driving on structured roads, we propose MultiNet-GS, a convolutional neural network model based on an encoder–decoder architecture that tackles multiple tasks simultaneously. We use the main structure of the latest object detection model, the YOLOv8 model, as the encoder structure of our model. We introduce a new dynamic sparse attention mechanism, BiFormer, in the feature extraction part of the model to achieve more flexible computing resource allocation, which can significantly improve the computational efficiency and occupy a small computational overhead. We introduce a lightweight convolution, GSConv, in the feature fusion part of the network, which is used to build the neck part into a new slim-neck structure so as to reduce the computational complexity and inference time of the detector. We also add an additional detector for tiny objects to the conventional three-head detector structure. Finally, we introduce a lane detection method based on guide lines in the lane detection part, which can aggregate the lane feature information into multiple key points, obtain the lane heat map response through conditional convolution, and then describe the lane line through the adaptive decoder, which effectively makes up for the shortcomings of the traditional lane detection method. Our comparative experiments on the BDD100K dataset on the embedded platform NVIDIA Jetson TX2 show that compared with SOTA(YOLOPv2), the mAP@0.5 of the model in traffic object detection reaches 82.1%, which is increased by 2.7%. The accuracy of the model in drivable area detection reaches 93.2%, which is increased by 0.5%. The accuracy of the model in lane detection reaches 85.7%, which is increased by 4.3%. The Params and FLOPs of the model reach 47.5 M and 117.5, which are reduced by 6.6 M and 8.3, respectively. The model achieves 72 FPS, which is increased by 5. Our MultiNet-GS model has the highest detection accuracy among the current mainstream models while maintaining a good detection speed and has certain superiority. [ABSTRACT FROM AUTHOR]

The feedstocks for conventional polyurea foams (PUrFs) are limited but they have more choices in urea route as discribed in this work. Urea, ternary amine T403 and diaminodecane DA‐10 were used for the construction of self‐foaming PUrFs, in which the byproduct ammonia performs as a foaming agent. The influence of nano SiO2 content and feeding ratio of T403 to DA‐10 on the pore morphology and the properties of PUrFs were investigated. A feeding amount of 3 wt% nano SiO2 is found to be able to significantly improve the nucleation performance of PUrFs. If the nano SiO2 loading is higher (5 wt%) the melt strength will increase, which is not benefit for the foaming. An appropriate feeding ratio of T403 to DA‐10 (1/1, sample PUrF‐3‐50) can improve the crystallinity thus promoting the nucleation in foaming process. The comprehensive performance of PUrF‐3‐50 is found to be the highest, with an average pore size at 429 μm, an apparent density at 298 kg/m3, and a compressive strength (75% strain) at 3.2 MPa. It is worth noting that the hysteresis of PUrF‐3‐50 is high (>90%) and stable. This work provides a new strategy for the preparation of PUrFs and broadens the applications of urea‐based polymers. [ABSTRACT FROM AUTHOR]

Pelaku perjalanan memiliki persepsi yang berbeda dalam menentukan arah rute perjalanan. Tujuan dari penelitian ini memprediksi pemilihan rute berdasarkan rute tercepat dengan mempertimbangkan variabel waktu tempuh, kecepatan rata-rata, dan derajat kejenuhan jalan. Metode yang digunakan terdiri dari kombinasi pemilihan rute dan pembobotan rute. Pembobotan rute tersebut dihitung menggunakan logika fuzzy metode Mamdani dengan menghasilkan bobot yang paling rendah. Penelitian dilakukan di Kabupaten Bogor dengan 1 asal dan 3 tujuan yang dihubungkan oleh 25 ruas jalan dan 19 titik simpul. Analisis yang dilakukan terdiri dari 2 skenario. Hasil analisis menunjukan bahwa rute yang dihasilkan berdasarkan skenario 1 dan skenario 2 berbeda. Hasil skenario 1 menggunakan waktu tempuh dan DS untuk asal 1 ke tujuan 2 melalui simpul 1-10-11-7-5-6-9-2 dengan nilai pembobotan sebesar 0,186, asal 1 ke tujuan 3 melalui simpul 1-10-12-13-14-3 dengan hasil pembobotan sebesar 0,224, dan asal 1 ke tujuan 4 melalui simpul 1-10-12-15-16-18-19-4 dengan hasil pembobotan sebesar 0,291. Sedangkan hasil skenario 2 kecepatan rata-rata dan DS untuk asal 1 tujuan 2 melalui simpul 1-10-11-7-8-9-2 dengan hasil pembobotan sebesar 0,198, asal 1 tujuan 3 melalui simpul 1-5-6-9-8-13-14-3 dengan hasil pembobotan sebesar 0,222, dan asal 1 tujuan 4 melaui simpul 1-5-6-9-8-13-12-15-16-17-19-4 dengan hasil pembobotan sebesar 0,208. Tingkat validasi program mencapai 99,91%.

To investigate pedestrian route choice mechanisms from a perspective distinct from that employed in discrete choice models (DCMs), this study utilizes machine learning models and employs SHapley Additive exPlanations (SHAP) for model interpretation. The data used in this paper come from several pedestrian flow experiments with two routes, which were recorded by UAV. Our findings indicate that logistic regression (similar to a binary logit model) exhibits good computational efficiency but falls short in predictive accuracy when compared to other machine learning models. Among the 12 machine learning models assessed, by calculating the new indicator named OP, we find that eXtreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB) strike the best balance between accuracy and computational efficiency. Regarding feature contribution, our analysis reveals that bottlenecks exert the most significant influence on pedestrian route choice behavior, followed by the time it takes pedestrians to return from the end of the route to the origin (reflecting pedestrian characteristics and attitudes). While the pedestrian density of the shorter route contributes less compared to bottlenecks and return time, it exhibits a threshold effect, meaning that once the density of the shorter route surpasses a certain threshold, most pedestrians opt for the longer route.

The deployment of hydrogen refueling stations is vital to the development of fuel cell vehicles. Past studies on the hydrogen refueling stations didn't consider congestion on paths and assumed all flows are on the shortest paths of OD pairs, which is not consistent with travelers' route choice behavior. In this paper, we study flow refueling location allocation based on the user-equilibrium (UE) status, which is consistent with travelers' route choice behavior. We present a flow-capturing location allocation (FCLA) model on UE conditions. We also presented an equilibrium algorithm avoiding cannibalization to solve the model. Based on the transportation network of Anaheim city in California in the US, we compared the results of applying a non-equilibrium method to the FCLA model and applying the equilibrium method to the FCLA model on UE conditions. The results of the two algorithms and models are different. The equilibrium model and algorithm can capture more flow and has a higher flow-capturing ratio. What's more, the equilibrium model and algorithm can fully capture the expected flow in practice since the real transportation network is basically in UE status. The equilibrium model and algorithm presented in this paper is the first study of this kind to locate stations in cities and is useful in the deployment of hydrogen refueling stations. • Study flow refueling location allocation based on the user-equilibrium (UE) status. • A flow capturing location allocation (FCLA) model on UE conditions. • An equilibrium algorithm avoiding cannibalization to solve the model. • Compare between non-equilibrium method and equilibrium method in flow capturing. [ABSTRACT FROM AUTHOR]

The article discusses the precarious situation of war-displaced Ukrainian citizens in Russia who lack international support and often rely on volunteer aid and conditional state assistance tied to acquiring Russian citizenship. It highlights the challenges they face in obtaining legal status in Russia and accessing essential services, and the limited role of international organizations due to restrictions imposed by the Russian government.

Migrant bird populations often show substantial variation in route choice and timing. Determining whether this population‐level variation is driven by between‐individual differences and/or flexibility within individuals is key to identifying drivers of migration patterns. 'Repeatability' (R, the proportion of population‐level variation attributable to between‐individual variation) has become a central metric for the relative consistency of individual behaviour. Individual repeatability in migratory route choice and timing is often reported to vary between seasonal and regional contexts and may also differ between demographic groups (e.g. sexes), but interpreting repeatability requires careful consideration of the underlying changes in between‐ and within‐individual variation. We GPS‐tracked repeat migrations for eight male and five female Eleonora's falcons Falco eleonorae and quantified the magnitude of within‐ and between‐individual variation and the individual repeatability of their seasonal routes and timing at 100 km intervals all across Africa. We did this across both sexes, and then separately for males and females. We found greater between‐individual variation in spring routes, albeit with substantial regional fluctuations in both seasons. The greatest between‐individual variation in routes occurred during the spring desert‐crossing, but this coincided with high within‐individual variation, and thus only low repeatability of route choice. Route repeatability instead peaked (R = 0.6–0.8) through the Horn of Africa in spring, and during the rainforest‐crossing in autumn. Variation and repeatability of timing was stable across regions, with generally higher between‐individual variation and repeatability in spring. Sex‐specific analyses suggest males exhibit higher route repeatability, while females exhibit stronger seasonal contrasts in timing repeatability. Such sex differences were unexpected, but overall, between‐individual variation and repeatability in routes and timings appear greater where environmental and annual cycle constraints are weaker. Route repeatability is especially high where falcons show fidelity to stop‐over sites, or individual barrier‐crossing preferences. Individual routines may be acquired through early‐life exploration‐refinement. [ABSTRACT FROM AUTHOR]

To enable travelers to reach their destination in a relatively short time while reducing travel costs, this paper establishes a multi-objective path optimization model by analyzing the impact of factors such as weather reduction coefficient and road congestion coefficient on travelers' route selection. And combine quantum genetic algorithm with an elite strategy to solve multi-objective models. Finally, the rationality of the model and algorithm is verified by a case. The results show that in the travelers' intermodal transport network considering bounded rationality, the choice of travel route will change with the change of road traffic congestion and weather conditions, and travelers need to make decisions according to the specific requirements of the day of departure. Using bounded rationality to describe travelers' expectations can reflect their travel needs well. [ABSTRACT FROM AUTHOR]

Exploring the relationship between leisure walking and the built environment will provide an improvement in human health and well-being. It is, therefore, necessary to explore the most relevant scale for leisure walking and how the association between the built environment and leisure walking varies across scales. Three hundred volunteers were recruited to wear GPS loggers, and a total dataset of 268 tracks from 105 individuals was collected. The shortest possible routes between starting and ending points were generated and compared to the actual routes using the paired T-test. An improved grid-based buffer approach was proposed, and statistics for the grid cells intersecting the paths were calculated. Grid cells were calculated for six scales: 50 m, 100 m, 200 m, 500 m, 800 m, and 1600 m. The results showed that the actual paths were on average 24.97% longer than the shortest path. The mean, standard deviation, and minimum and maximum values of the built environment variables were all significantly associated with leisure walking. The most relevant spatial scale was found to be the 100 m scale. Overall, the smaller the scale, the more significant the association. Participants showed a preference for moderately compact urban forms, diverse options for destinations, and greener landscapes in leisure walking route choice. [ABSTRACT FROM AUTHOR]

The operations performance of a railway station depends on the compatibility of its layout and the traffic pattern. It is necessary to determining an adaptable station layout for a railway station in accordance with its complex traffic pattern during the design phase. This paper assesses the railway station layout from a capacity perspective. In particular, this paper addresses an optimization-based capacity estimation approach for the layout variants of a railway station (i.e., the number of siding tracks and the structure of the connections in between) considering the traffic pattern variants. A mixed integer programming model for microscopic timetable compression is applied to calculate the occupation rate of the given traffic pattern with flexible route choices and train orders. A novel "schedule-and-fix" heuristic algorithm is proposed to solve large-scale instances efficiently. In the case study, we evaluate the performance of the schedule-and-fix method compared with the benchmark solvers Gurobi and CP-SAT. Applying the proposed method, we compare the capacity performances of the two station design schemes, i.e., one with a flyover and the other without. The result shows that, for the given instance, building a flyover gains capacity benefits as it reduces the potential conflict in the throat area. However, the level of benefit depends on the combination of trains. It is necessary to build the flyover when the proportion of turn-around trains is more than 70% from the perspective of station capacity. [ABSTRACT FROM AUTHOR]

Optimization of the travel paths is necessary to shorten travel time and reduce travel costs. This paper proposes the analysis theory of the travelers' low carbon route choice behavior based on improved prospect theory by analyzing the punctuality rate of travel time, the travelers' comfort level on the travelers' intermodal transport route optimization. Dual reference points are set according to the whether travelers have the prior experience as the judgment basis, constructing an optimization model of travelers' intermodal transport under bounded rationality. The results show that, in the travelers' intermodal transport route considering carbon emissions, the travel route selected by travelers will change with the road state. In this problem, the gain sensitivity coefficient α and the loss sensitivity coefficient β have influence on the decision. When traffic crowding or bad weather occurs, travelers tend to be more sensitive to time. The setting of each parameter has an important impact on the comprehensive prospect values. [ABSTRACT FROM AUTHOR]

Animals must make route choices every day when moving through their habitat while foraging. Choosing an optimal route can be cognitively costly, and primates and other animals have been shown to use simple heuristics, "rules of thumb", to make foraging route choices. We investigated the potential use of heuristics among foraging free-ranging Japanese monkeys (Macaca fuscata) during solitary foraging trials. We also investigated the potential influence of individual variables (age and sex) and social variables (presence in the central group, presence of potential inter- and intraspecific competitors), on the use of heuristics, route length and trial time. We used a multi-destination foraging experiment with 6 platforms in a (4 m × 8 m) Z-array, completed by 29 Japanese macaques in 155 runs at the Awajishima Monkey Center in Japan. Our results showed that the macaques chose routes consistent with heuristics (e.g. nearest neighbour heuristic 19.4%, convex hull heuristic 4.5%) and selected optimal routes (shortest path in 23.9% of the trials). We also identified a potential new heuristic that was used most frequently, that we termed the "sweep heuristic" (27.1% of trials), which we interpreted as a strategy to deal with competitive foraging trade-offs – choosing routes to prioritize not leaving isolated food pieces behind. Age was significantly related to trial time; juvenile macaques were faster than adults and young adults, using speed to gain access to resources. Solitary trials with conspecifics present took significantly longer routes. Our results suggest that contextual factors led to variation in Japanese macaque decision-making, and we suggest that the preferential use of a sweep heuristic may have been a response to high intragroup competition. [ABSTRACT FROM AUTHOR]

Analysing user behaviour and thus travel mode choice is an important task in transport planning and policy-making in order to understand and predict travel demand. Recent pandemic events have challenged the modal choices of European users by reducing the use of public transport at various times and favouring walking and/or the use of electric bikes and scooters for last-mile travel. A number of studies have focused on analysing how the pandemic affected workers' choice of transport mode, with particular reference to local public transport, by developing multinomial logistic regression and artificial neural network models to analyse travellers' choice of transport mode before and after COVID-19. Particularly in non-European contexts, studies have been conducted on the relationship between socio-economic factors and the duration of e-scooter trips before and during the health crisis caused by the pandemic. in these contexts, a general increase in the duration of escooter trips after the pandemic was shown. Few studies, however, have analysed the European context. Several factors relating to services and infrastructure as well as sociodemographic components contributed to the propensity to use e-scooters as evidenced by a number of literature works in the European context. However, little research has been conducted using the machine learning approach to understand which factors and how they may influence modal choices. The present research work focused on the analysis of last-mile transport choices by investigating the propensity of a sample of users residing in Sicily during different time phases before and after the COVID-19 pandemic. In this study, 35 different classes were determined for a total of 545 data. The classification process was carried out using SMO, KNN and RF machine learning algorithms. The results showed a reduction in the frequency of e-scooter use during the health crisis caused by the pandemic. The results showed that this was a temporary behaviour, even though the purpose of e-scooter use by most individuals changed during the health crisis caused by the pandemic. However, it was observed that the frequency of e-scooter use decreased in most individuals during the health crisis caused by the pandemic and this became a permanent behaviour. The results suggest that the analysis of the importance of variables in relation to different periods and is essential for a better understanding and effective modelling of people's travel behaviour and for improving the attractiveness of these means of transport for companies operating services in the areas examined. [ABSTRACT FROM AUTHOR]

There is clear evidence of the effects of air pollution on health. In this paper, we present an innovative application designed to assess Air Quality (AQ) exposure based on the World Health Organization's AQ Guidelines, analysing pollutants and their concentrations independently. Our aim is to provide this information to citizens based on their health profile (medical history or requirements) before and during outdoor trips of their choice, both walking and cycling, empowering them to proactively make informed personal decisions about their route choices and identifying potentially unhealthy travel environments. For this purpose, we have access to official data from AQ monitoring stations that are updated periodically every 10 min. Then, by using spatial interpolation techniques (with Ordinary Kriging), we estimate each pollutant over a grid superimposed on the city map. Once the pollutants have been mapped on each route, they are analysed in order to consider the different alternatives for deciding and planning changes in speed or trajectory. We evaluated the application in the city of Valencia (Spain) as a use case under different scenarios, and showed the results to assess exposure to pollution on the routes of citizens. [ABSTRACT FROM AUTHOR]

The Cell Formation Problem (CFP) involves the clustering of machines to enhance productivity and capitalize on various benefits. This study addresses a variant of the problem where alternative routes and machine reliability are included, which we call a Generalized Cell Formation Problem with Machine Reliability (GCFP-MR). This problem is known to be NP-Hard, and finding efficient solutions is of utmost importance. Metaheuristics have been recognized as effective optimization techniques due to their adaptability and ability to generate high-quality solutions in a short time. Since BWO was originally designed for continuous optimization problems, its adaptation involves binarization. Accordingly, our proposal focuses on adapting the Black Widow Optimization (BWO) metaheuristic to tackle GCFP-MR, leading to a new approach named Binary Black Widow Optimization (B-BWO). We compare our proposal in two ways. Firstly, it is benchmarked against a previous Clonal Selection Algorithm approach. Secondly, we evaluate B-BWO with various parameter configurations. The experimental results indicate that the best configuration of parameters includes a population size (P o p) set to 100, and the number of iterations (M a x i t e r) defined as 75. Procreating Rate (P R) is set at 0.8, Cannibalism Rate (C R) is set at 0.4, and the Mutation Rate (P M) is also set at 0.4. Significantly, the proposed B-BWO outperforms the state-of-the-art literature's best result, achieving a noteworthy improvement of 1.40%. This finding reveals the efficacy of B-BWO in solving GCFP-MR and its potential to produce superior solutions compared to alternative methods. [ABSTRACT FROM AUTHOR]

Real‐time individuals' destination prediction is of great significance for real‐time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this paper, taking the public transportation metro systems as example, we design a practical method called practical model with strong interpretability and predictability to predict each passenger's destination. Our main novelties are two aspects: (1) We propose to predict individuals' destination by combining personal and crowd behavior under certain context. (2) An explanatory model combining discrete choice model and neural network model is proposed to predict individuals' stochastic trip's destination, which can be applied to other transportation analysis scenarios about individuals' choice behavior such as travel mode choice or route choice. We validate our method based on extensive experiments, using smart card data collected by automatic fare collection system and weather data in Shenzhen, China. The experimental results demonstrate that our approach can achieve better performance than other baselines in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Currently, preventing epidemics is an extremely critical global topic. Using present data to quickly conduct virus simulations is a difficult but interesting problem, especially when real situations are difficult to experimentally demonstrate. In the past, most studies have used package software for disease transmission simulation, but this approach is limited by availability and software cost. Therefore, we propose a visual simulation of disease transmission using building information modeling data and a 3D model using Unity. The results show that the proposed method can effectively predict the probability and route of disease transmission; it also verifies that the vertical pipeline on the floor plane is conducive to the spread of the virus (90%), and disease transmission on the plane gradually expands outward from the starting room and has a higher probability of spreading (80%) from the opposite room. In addition, a vertical pipeline was simulated using a toilet exhaust air ventilation pipeline, from which it can be observed that the adjacent floors have a higher diffusion probability (70%). It has also been confirmed that distance is the primary factor affecting disease transmission. This framework may provide designers and managers further protection against the spread of future epidemics. [ABSTRACT FROM AUTHOR]

Mitigation measures for downstream migratory Atlantic salmon smolts (Salmo salar L.) at migration barriers usually turn out to be insufficient to enable safe and quick passage, probably due to a lack of knowledge about their behavioural responses. Combining manual 2D tracking with hydrodynamic modelling has been rarely performed but might be useful to highlight environmental factors influencing smolt behavioural tactics and the choice of a migration route. We investigated the smolt downstream migration at a hydropower plant that offers five migration routes, including a Kaplan turbine and a fish-friendly Archimedes screw. Four behavioural tactics were defined to describe the smolt expressed behaviour, which was mainly complex and hesitant. The majority of the smolts approached more than one migration route before crossing the site and the Kaplan turbine turned out to be the most approached route, contrary to the Archimedes screw. Hydrodynamic modelling highlighted that flow velocity and water depth were used as hydraulic cues in the selection of a migration route, as the smolts preferred higher flow velocities and water depths. The comprehension of the factors influencing the research behaviour at hydropower plants may be useful to design attractive mitigation measures and to guide the smolts efficiently towards safe routes. [ABSTRACT FROM AUTHOR]

The frequency of natural and man-made disasters has increased over the past few decades, which has doubled the significance of evacuation planning because it directly affects people's lives and properties. How evacuees behave during a disaster and the methodologies to assess their behaviour are vital factors in managing any emergency scenario. For example, during the evacuation of a transportation network, users' amount of information and how they react are imperative to achieve a resilient response to a disaster. Even though there are numerous approaches for assessing evacuee behaviour, further research is needed to determine how and when each methodology should be employed. This paper intends to evaluate and classify the methodologies that have been presented up to date to create a more consistent approach to interpreting human behaviour during an evacuation process. The majority of studies focus on how people behave during an evacuation of a building, with less attention paid to how they behave during a transport evacuation. Among the proposed methodologies in this paper, the virtual reality approach, in individual behaviour, and agent-based models, in crowd behaviour, have more advantages than other approaches. Overall, a comparison between the proposed approaches is made in the discussion part. The output of this study provides the classifications and suggestions for researchers to pick an appropriate approach based on the types of problems, and some direction for future studies are introduced. To reach the research goal, 177 papers have been reviewed between 1954 and 2022. [ABSTRACT FROM AUTHOR]

This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a new estimation method based on the idea that the choice probabilities of unconnected link observations can be exactly computed by solving systems of linear equations. We further design a new algorithm, called decomposition–composition (DC), that helps reduce the number of systems of linear equations to be solved and speed up the estimation. We compare our proposed algorithms with some standard baselines using a dataset from a real network, and show that the DC algorithm outperforms the other approaches in recovering missing information in the observations. Our methods work with most of the recursive route choice models proposed in the literature, including the recursive logit, nested recursive logit, or discounted recursive models. • Estimation of route choice models with incomplete trip observations. • A new algorithm based on Expectation–Maximization. • A new method to exactly and quickly compute the likelihood function. [ABSTRACT FROM AUTHOR]

The efficiency of the planning process in public transport is represented through different measures, which are practically impossible to optimize simultaneously. This study defines a bi-objective optimization problem for the transit network design to analyze the trade-off between minimization of travel times and reducing monetary costs for passengers (which was not addressed in the literature) while considering a hard constraint for operational costs. Indeed, the minimization of monetary costs for passengers is relevant in transport systems without a complete integrated fare system, where passengers may pay for each trip-leg; thus, modeling monetary costs for users is essential when referring to the system's accessibility and route choice. To achieve our goal, we implement an epsilon-constraint algorithm capable of obtaining high-quality approximations of the Pareto front for benchmark instances in hours of computational time, which is reasonable for strategic planning problems. Numerical results show that the conflict between both objectives is evident, and it is possible to identify the more useful lines to optimize each objective, leading to relevant information for the decision-making process. Finally, we perform a sensitivity analysis on the budget parameter of our optimization problem, showing the classic trade-off between the operational costs and the level of service in terms of travel time and monetary cost. • Our study defines a bi-objective problem for the transit network design. • This study analyzes the trade-off in reducing travel times and monetary costs. • We identify the lines that have a more significant impact on each objective. • Our model is a flexible tool to obtain information about transport systems. [ABSTRACT FROM AUTHOR]

This paper proposes a model-less feedback system driven by tourist tracking data that are automatically collected through mobile applications to visualize the gap between geomedia recommendations and the actual routes selected by tourists. High-frequency GPS data essentially make it difficult to interpret the semantic importance of hot spots and the presence of street-level features on a density map. Our mobile collaborative framework reorganizes tourist trajectories. This processing comprises (1) extracting the location of the user-generated content (UGC) recording, (2) abstracting the locations where tourists stay, (3) discarding locations where users remain stationary, and (4) simplifying the remaining points of location. Then, our heatmapping system visualizes heatmaps for hot streets, UGC-oriented hot spots, and indoor-oriented hot spots. According to our experimental study, this method can generate a trajectory that is more adaptable for hot street visualization than the raw trajectory and a simplified trajectory according to its geometry. This paper extends our previous work at the 2022 IEEE International Conference on Big Data, providing deeper discussions on application for local tourism. The framework allows us to derive insights for the development of guide content from mobile sensor data. [ABSTRACT FROM AUTHOR]

Real-world route navigation data indicate that nontrivial portion of drivers do not prefer the system-recommended best routes. Current navigation systems have simplified assumptions about drivers' route choice preferences and do not adequately accommodate drivers' heterogeneous route choice preferences, mainly because of: (i) difficulty in acquiring exogenous criteria (e.g., sociodemographic information) that are typically used to differentiate drivers' preferences in behavioral modeling; and (ii) difficulty in capturing preference of individuals due to limited preference data at the individual level. To address these, this paper introduced a human-centric machine learning technique named Multi-Task Linear Classification Model Adaption (MT-LinAdapt). It can capture drivers' common aspects of route choice preferences and yet adapts to each driver's own preference. In addition, any evolvement of individual drivers' preferences can be simultaneously integrated to update the common preference for further individual drivers' preference adaptation. This paper evaluated MT-LinAdapt against two state-of-the-art route recommendation strategies including an aggregate-level and an individual-level data-based strategies, which are categorized based on the data used for modeling. With a real-world dataset containing 30,837 drivers' navigation usage data in Daegu City, South Korea, MT-LinAdapt was compared to existing strategies for its performance at different levels of data availability, and showed at least the same performance with existing strategies when minimum preference data is available and achieves up to 7% higher prediction accuracy as more data becomes available. Higher prediction accuracies are expected to bring better user satisfaction and compliance rates which can further help with transportation system control and management strategies. [ABSTRACT FROM AUTHOR]

Urban transportation systems involve thousands of individuals making choices between routes with multiple modes and transfers. For transportation system simulations to produce realistic results, modelers need to incorporate these users and their choices. Choice‐based conjoint surveys provide an attractive solution for obtaining flexible utility models that can be used to predict choices for a wide variety of trips. In this study, we demonstrate an example using conjoint survey data of commuter mode choice in the Washington, D.C. metro area (N = 1651). We sample commuters who primarily drive and those that take transit. We examine preferences for different types of multimodal trips, including those with intramodal and intermodel transfers. We find that trips involving a bus transfer are the least preferred while both drivers and transit users both value metro similarly to driving. We also find that walking during transit trips is an important barrier, with the travel time penalty for walking being 60% higher than that of time in a vehicle. Our findings highlight the significance of accounting for differences in modal transfer types in transportation system simulations. Reducing arrival time uncertainty was not a significant factor in commuter mode choice, and commuters' value of time was similar across all vehicle types, suggesting that increasing the relative speed of transit modes may only have a marginal effect on commuter substitution away from personal vehicles. [ABSTRACT FROM AUTHOR]

This study proposes a modified cellular automaton to simulate passenger ship fire evacuation. A fire dynamics simulator (FDS) is used to simulate the firing process for the passenger ship. The generated carbon monoxide (CO) concentration, temperature, and visibility are converted into equivalent lengths and input to the equivalent field of the modified cellular automaton. The fire field is applied to keep the evacuees away from the fire. And game theory is adopted to solve the conflict in the choice of route for evacuees. With or without fire on the passenger ship, three different evacuees' densities are used to study the influence of different proportions of evacuees who are familiar with exits during the evacuation time. The positions of exits are also studied. The results show that when the fire release rate increases from 3000 to 8000 kW s−2 for the same evacuation scenario, the earliest time that evacuees are in danger is 50% earlier. The impact of fire on evacuees is greater than that of evacuees' density. In the case of two close exits or exits close to the bulkheads, the congestion near exits is obvious and has the greatest effect on the evacuation time of the high evacuees' density. [ABSTRACT FROM AUTHOR]