Air pollution is one of the most important branches of environmental science as it affects human health, climate and ecosystems. Emissions of air pollutants from transport (vehicles and ships) in port cities strongly affect air quality at local scales, warranting for a combination of theoretical and experimental studies to identify pollution hotspots. The purpose of this paper is to provide a methodology for developing a hybrid emission inventory from transport sector for two port cities located respectively on the Northern Aegean islands of Chios and Lesvos. Emission inventories were constructed for the year 2014 based on top-down and bottom-up approaches. Official data from local authorities and survey results were used for the calculation of emissions. Traffic emissions were spatially allocated to the road network based on population data and hourly traffic counts, and distributed over time (on an hourly basis) with the use of local temporal coefficients. Regarding carbon monoxide road emissions, the highest quantities are mainly emitted by Passenger Cars (43%,32% in Chios and Lesvos respectively) while for PM10 emissions, trucks have the largest share (66% in Chios and 86% in Lesvos). The pollutants that are emitted in greater quantities from the ships at the ports of Mytilene and Chios are NOx, followed by SO2 and CO. Most of the ship emissions in the ports occur by the ships at berth, as they remain berthed for hours whereas maneuvering lasts 15e20 min. As for the daily contribution of the two transport sources to the pollution profile of Mytilene, road emissions are higher for almost all pollutants. However, the contribution of ship emissions is not negligible, especially during the touristic period when marine traffic increases and emissions close to the port area become more important than those from road transport. @en

Workload forecasting related to truck arrivals is of essential importance not only for optimal resource allocation but also for encountering delays and bottlenecks in the landside operations of terminals. In order to increase their efficiency, some ports have tried to implement various methods such as Truck Appointment Systems (TAS) but, in general, information on the arrival times of trucks to pick up containers remains unreliable and scarce. This paper sought to develop pick-up time-of-day models for import containers using data easily retrieved from Terminal Operating Systems (TOS). Model results indicate that the receiver of goods and container characteristics are among the main factors affecting pick-up time-of-day. Differentiation is observed between the different days of the week. The developed Time-Of- Day (TOD) models can be used to calculate the probability of drayage truck arrival times. The application of the proposed methodology proves helpful when reliable information of truck arrivals is unavailable, and can also be used alongside TAS implementation to assist terminal operators.

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Introduction In container terminals, hinterland workload forecasting is of essential importance for storage planning, daily and hourly equipment allocation and human resources management. It has been observed that one of the main factors increasing the inefficiency of a container terminal is unproductive moves (UPM), i.e. relocations of containers for any purpose beside inspection and customs. Several studies have illustrated that accurate information provisions on drayage truck arrivals can result in an important reduction of Unproductive Moves (UPMs) (Goodchild and Noronha, 2010). The number of UPMs can be applied as Key Performance Indicator (KPI) to measure terminal efficiency. Therefore, based on the prediction of the Dwell Time (DT) of a container, a daily pick-up probability can be assigned to each container depending on its arrival day. This information would permit stowage officers to stack the containers in such a manner that the containers with the higher pick-up probabilities could be retrieved easily without requiring extra UPMs. Literature review revealed a limited research on the factors that affect the DT of containers (Rodrigue, 2008; Moini et al., 2012). Furthermore, research on freight behavioural modelling literature pointed out reliability as one of the most important factors that influence the choices related to the transportation of the different products (Ben-Akiva et al., 2015; Feo et al., 2011; Fries, 2009). In the context of this research we considered Freight Forwarders (FFs) as the key decision makers that determine the decision of when to pick-up an import container from a port container terminal and developed a Hybrid Choice Model (HCM) where we inserted the “importance of reliability” as a latent variable. Modelling Framework We developed a questionnaire based survey that addressed factors that may influence their decisions on when to pick-up a container from a terminal. Specifically, we requested general information on the main characteristics of the FF and the description of a typical import pick-up from the container terminal. In addition we asked FFs to reply on statements about how they perceive their firm’s reliability. In the last part of the questionnaire we designed an SP experiment were respondents were asked to state how many days, after getting customs clearance, they would leave import containers in the terminal before picking them up. We collected data from 34 FFs in the Middle East during August 2015. Each FF was presented with 8 different scenarios and our total sample consists of 264 observations. The proposed HCM includes an explanatory variable that cannot be directly measured; this is the latent variable which describes the importance that FFs give to the reliability of the services their company offers to clients. For the development of the latent variable model two types of equations are necessary: the measurement equations that link the indicators to the latent variable and the structural equation that quantifies the influence of the company’s socioeconomic characteristics to the latent variable (Ben-Akiva et al., 2002; Walker and Ben-Akiva; 2002; Tsirimpa et al., 2009; Kamargianni and Polydoropoulou, 2014; Kourounioti and Polydoropoulou, 2015). The structure of the HCM is shown in Figure 1 in which the complete set of structural and measurement equations is sketched depicting the relationships between explanatory variables and each partial model.FIGURE 1 HCM Model Structure For the development of the choice model the continuous DT was divided into the following discrete time intervals: 1.Interval 1: Duration 0-1 days 2.Interval 2: Duration 2-6 days 3.Interval 3: Duration 7 days 4.Interval 4: Duration 8-9 days 5.Interval 5: Duration over 9 days For the development of the HCM we made the assumption that the importance a FF gives to providing reliable services influences the decision related to DT. We expect that the higher the importance of reliability for the operations of the company the sooner the container will be picked up from the terminal. Apart from the latent variable in the choice model we inserted: •the container characteristics: oContainer type: dummy variable equal to if it is a 20’ft container or not oRoyal Client: dummy variable equal to 1 if the client to whom the container belongs is a royal client of the company, meaning that uses only the specific FF to execute transportation of his/hers containers. •seasonality oSpring: dummy variable equal to 1if the pick-up is realised in spring. oMonday: equals to 1 when the container is discharged from the customs inspection on Monday. oWarehouses: dummy variable equal to 1 when the FF owns warehouses. •Rel= latent variable “importance of reliability”. •A disturbance effect (η) term was inserted to account for the panel effect. •The error term ε.We assumed that the “importance of reliability” depends on the following characteristics of the FF which we inserted in the structural equation of the latent variable model: •Less than 10 employees: dummy variable equal to 1 when the FF company has less than 10 employees. •More than 30 employees: dummy variable equal to 1 when the FF company has more than 30 employees. • Delayed deliveries: dummy variable equal to 1 when the FF company delivers more than once per week delayed deliveries. •Scheduled service: dummy variable equal to 1 when the FF executes a scheduled service to the container terminal. •ω= random distribution of errors.Finally, respondents were asked to state the level of their agreement with the statements in Table 2. The FFs of the sample disagreed that they ensure on-time deliveries only when monetary fines are imposed by the clients. They disagree that delayed shipments only to their good clients may harm the reliability of their companies. In addition, they state that they are willing to accept additional measures from the container terminals in order to guarantee the on-time and undamaged delivery of containers. These statements were inserted as indicators in the equations of the latent variable measurement model. Table 2. Indicators of the Latent Variable “Importance of Reliability” Latent Variable=Importance of Reliability State your level of agreement using a scale (1= totally disagree….7= totally agree)MeanStd. Dev. I would be willing to comply with additional measures to increase a container's safety6,241,044 I would be willing to comply with additional measures to decrease delays. 6,050,590 I try to avoid delays only when there are monetary fines imposed by the client3,433,187 It is important to be able to inform my client on time when a container will be delivered.6,150,498Model Results Model estimations were conducted using Python Biogeme 2.4. The results of the choice model are presented in the table below. Table 3. HCM results Parameterst-stat βoβo1 -1,09-2,82 βo2-2,93-2,25 βo3-5,52-3,38 βo4-5,73-3,00 βtwentyΒtwenty1-0,876-1,39 Βtwenty2-0,699-1,55 Βtwenty3-0,915-1,84 Βtwenty4-1,52-2,13 βroyal_client Βroyal_client1 0,5611.97 Βroyal_client20,3520,77 Βroyal_client30,4811,99 Βroyal_client4-0,484-1,86 βMondayβMonday10,4420,83 βMonday20,5380,98 βMonday31,572,83 βMonday4-0,6-1,96 βspringβspring10,4371,12 βspring20,060,94 βspring30,5761,98 βspring4-1,15-2,34 β<100 β<10010,04680,08 β<1002-1,31-2,85 β<1003-0,763-1,56 β<1004-1,11-2,00 Βwarehouses βwarehouses10,8702,04 βwarehouses20,1530,66 βwarehouses30,1990,44 βwarehouses4 -0,701-1,95 γrelγrel10,8544,37 γrel20,6463,16 γrel30,1232,15 γrel40,4602,85 γrel50,490 2,08 Η13,39 Sample size254 R20,357 Model results showed a negative correlation between the 20’ft containers and the utility in all time intervals. Containers that belonged to a royal client of a company are picked up faster and presented higher utility in the lower time intervals. In addition, when the container was discharged from customs early in the week the utility of earlier time intervals increased. Pick-ups during spring are conducted faster. FFs without warehouses did not use the terminal as a storage and presented negative correlation in the last time intervals Companies that conducted less than 100 pick-ups per month tended to leave the containers for less time in the terminal. This variable has a negative sign only in the last time intervals. As we can see for the value of tstat, the latent variable “importance of reliability” influences the DT model. The βς of the latent variable decreased as the DT intervals increased. The model results agreed with our initial assumption that the more important reliability is for a FF the faster the pick-up will be conducted. In the structural equation of the latent variable “importance of reliability” we insert the characteristics of the FF (Table 3). The results of the model showed that the companies with less than 10 employees desired to be more reliable. This can be explained either by the close relationships they develop with their clients or by their need to attract more clients. On the contrary, larger companies with more than 30 employees seem to give less importance to their reliability. The FFs that admitted to frequently face delayed deliveries seem to be less sensitive to providing reliable and trustworthy services. Finally, FFs that believed that reliability is very important to their clients operate a scheduled service for pick-ups to the terminal in order to be able to serve their clients better. Table 3. Results of the structural and measurement model of the latent variable Structural Model Parameterst-stat β_rel5,0330,69 θ<10_employees.0,1416,60 θ>30_ employees.-0,763-6,77 θdaily_deliveries-0,368-6,75 θsceduled_service0,0247-4,35 σrel0,821 6,77 Measurement Model Parameterst-stat a10,00a2-4,58-3,11 a3-2,932,39 a46,6675,309 λ1 1,00λ22,076,77 λ30,80610,150 λ41,29620,923 υ12,4822,29 υ20,060622,39 υ32,3831,69 υ4-0,214-2,726Research Implications Terminal operators face a lack of information from the landside transportation parties on how many and which containers will be picked-up every day. Undoubtedly, the accurate prediction of the next day’s tasks would also lead to the optimal allocation of equipment and human resources to avoid overproviding or underproviding equipment and personnel. Excess resources could lead to higher and highly unproductive operational costs. Lack of resources could cause delays on service, congestion inside the terminal and the surrounding road network, which ultimately leads to unsatisfied customers. In addition, being able to understand the determinants of DT can be useful when designing efficient policies to control the amount of time containers spend in the terminal before being picked up. Because many shippers do not own their own storage facilities, as well as low demurrage fees, many shippers choose to keep their cargoes in the terminal’s yard; however, this practice impacts terminal capacity. Therefore, the ever-growing volume of transported cargoes, in combination with the lack of available space for terminal expansion, is expected to force terminal operators to enforce monetary policies or various operational restrictions such as delivery or pick-ups after appointment or higher demurrage fees.References Ben-Akiva, M., Bolduc, D. and J. Park , (2013). “Discrete Choice Analysis of Shippers’ Preferences.”Freight Transport Modeling, ed. Ben-Akiva, M., H. Meersman, and E., van de Voorde, Emerald Group Publishing Limited, United Kingdom. Ben-Akiva, M., Walker, J., Bernardino, A., Gopinath, D., Morikawa, T. and A. Polydoropoulou, (2002). “Integration of Choice and Latent Variable Models.” Perpetual motion Travel behavior research opportunities and application challenges, In H. Mahmassani (Ed.), Elsevier, Oxford, United Kingdom. Bierlaire, M. (2003). “BIOGEME: A free package for the estimation of discrete choice models”. Presented at the 3rd Swiss Transport Research Conference, Ascona. Bierlaire, M. (2015). “BisonBiogeme: estimating a first model.” Technical report TRANSP-OR 150720. Transport and Mobility Laboratory, ENAC, EPFL. Feo, M., Espino, R., and L. Garcia, (2011). “A stated preference analysis of Spanish freight forwarders modal choice on the south-west European Motorway of the Sea.” Transport Policy, vol. 18, is. 1, pp. 60-67. Fries, H., (2009). “Market potential and value of sustainable freight transport chains.” Zurich: ETH Zurich. Goodchild, M. and P. Val Noronha, (2010). “MeTrIS: Metropolitan Transportation Information System: Applying Space Based Technologies for Freight Congestion Mitigation”, U.S. Department of Transportation, Final Report.Kamargianni, M., and A. Polydoropoulou, (2014). “Generation’s Y Travel Behavior and Perceptions Towards Walkability Constraints among Three Distinct Geographical Areas.” Presented in the 93rd Annual Meeting of the Transport Research Board of the National Academies, Washington D.C.. Kourounioti, I. and A. Polydoropoulou, (2015). “Understanding Freight Forwarders Time-of-Day Choice Decision Making Framework- A Greek Case Study.” Paper presented at Transportation Research Board, January 2015. Moini, N.,M. Boile, S. Theofanis and W. Levanthal, (2008). “Estimating the determinant factors of container dwell times at seaports”, Maritime Economy and Logistics, vol. 14, pp. 162-177@en

The general aim of this paper is to identify the key factors that affect the Dwell Time (DT) of containers and to quantify the influence of terminal policies on the DT. Aggregate data were collected from the Terminal Operation Systems (TOS) of three container terminals; two in the Middle East and one in Asia. The Poisson regression models that were developed for each terminal revealed the factors affecting the Dwell Time (DT). Terminal charging policies and customs inspection affect DT especially in ports that impose storage fees from the first day and adopt more efficient customs inspection methods. In addition, DT was found to depend on: 1) container s weight; 2) container status (full or empty); 3) billable line; 4) seasonality, and; 5) pick-up day of the week. Information on the receiver of goods and the commodity was available and incorporated in the Poisson regression models resulting in higher R2 and better model applicability. The combined model of the three terminals explains the influence of the different monetary policies and the terminal type on the DT. The developed models can be used to predict the DT and consequendy the day an import container is to be picked-up from the terminal. Model results highlight the importance of collecting information on the commodity and the receiver of the goods for the de-velopment of predicting models that enhance decision making in port container terminals both in an operational and in strategic level.

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